{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "004b6469-9d6a-47ce-84b5-2c3b9746005a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 b2fc1a21 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete ✅ (112 CPUs, 503.5 GB RAM, 28.1/30.0 GB disk)\n"
     ]
    }
   ],
   "source": [
    "import comet_ml\n",
    "import torch\n",
    "import utils\n",
    "\n",
    "comet_ml.init(project_name='exp_100epoch')\n",
    "# 这里应该会包含100epoch的0,0.6,1.2加雾以及各个以100epoch为单位的增量\n",
    "display = utils.notebook_init()  # checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9445a331-7b0c-47e3-83b8-35d1eee19212",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=yolov5s.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=baseline_VOC, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 b2fc1a21 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/09a757b84d9d4072b3b811748eb74e22\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from yolov5s.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 16551 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/baseline_VOC/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/baseline_VOC\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.64G      0.111    0.03841    0.08407         68        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/49      3.64G    0.05996    0.03479    0.04811         36        640: 1\n",
      "tensor([0.71923], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.665      0.691      0.719      0.414\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.22G    0.04643    0.03048    0.02095         58        640: 1\n",
      "tensor([1.06632], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.69       0.68      0.725      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.22G    0.04505    0.03277    0.02086         37        640: 1\n",
      "tensor([0.71560], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.592      0.538      0.559      0.306\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.22G    0.04501    0.03519    0.02428         46        640: 1\n",
      "tensor([0.68710], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.579       0.55      0.563      0.313\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.22G    0.04416    0.03523      0.023         39        640: 1\n",
      "tensor([0.71810], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.645      0.595      0.625      0.352\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.22G    0.04265    0.03475    0.02112         28        640: 1\n",
      "tensor([0.62009], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.645      0.627      0.655       0.38\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.22G    0.04153     0.0344    0.01977         39        640: 1\n",
      "tensor([0.68525], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.647      0.621      0.652      0.386\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.22G    0.04078    0.03391    0.01935         34        640: 1\n",
      "tensor([0.55527], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.682      0.644      0.689      0.415\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.22G    0.04012    0.03352     0.0182         41        640: 1\n",
      "tensor([0.59413], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.686      0.648      0.695       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.22G    0.03932    0.03277    0.01751         46        640: 1\n",
      "tensor([0.61124], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.707      0.671      0.724      0.443\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.22G     0.0388    0.03286    0.01666         30        640: 1\n",
      "tensor([0.63734], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.727      0.663      0.728      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.22G    0.03821    0.03288    0.01624         26        640: 1\n",
      "tensor([0.56983], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.725       0.69      0.743      0.466\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.22G    0.03757    0.03239    0.01523         33        640: 1\n",
      "tensor([0.73467], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.715      0.693      0.739      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.22G     0.0373    0.03217    0.01476         30        640: 1\n",
      "tensor([0.60539], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.733      0.701      0.758      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.22G    0.03716    0.03185     0.0143         33        640: 1\n",
      "tensor([0.67936], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.719      0.769      0.496\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.22G    0.03642    0.03161    0.01398         23        640: 1\n",
      "tensor([0.48984], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.731      0.702      0.756      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.22G     0.0361    0.03135    0.01392         40        640: 1\n",
      "tensor([0.58566], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.717      0.774      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.22G    0.03573    0.03128    0.01348         45        640: 1\n",
      "tensor([0.52129], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.746      0.717      0.777      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.22G    0.03555     0.0312    0.01313         20        640: 1\n",
      "tensor([0.38032], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.737      0.731      0.778      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.22G     0.0351    0.03101    0.01254         38        640: 1\n",
      "tensor([0.60073], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.733      0.736      0.778      0.516\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.22G    0.03484    0.03052    0.01214         33        640: 1\n",
      "tensor([0.57957], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771      0.724      0.792      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.22G    0.03468    0.03051    0.01197         28        640: 1\n",
      "tensor([0.44174], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.736      0.796      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.22G    0.03424    0.03044     0.0115         27        640: 1\n",
      "tensor([0.43022], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.743      0.799      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.22G    0.03404    0.02988    0.01156         29        640: 1\n",
      "tensor([0.68047], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.741      0.796      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.22G    0.03394    0.02999    0.01099         35        640: 1\n",
      "tensor([0.37775], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.752      0.801      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.22G    0.03347    0.02965    0.01102         31        640: 1\n",
      "tensor([0.36578], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.776      0.745      0.806      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.22G    0.03346    0.02955    0.01094         40        640: 1\n",
      "tensor([0.66227], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.759      0.805      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.22G    0.03306     0.0297    0.01029         29        640: 1\n",
      "tensor([0.51596], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.779      0.751      0.809      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.22G    0.03296    0.02929    0.01028         26        640: 1\n",
      "tensor([0.40895], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.751      0.806       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.22G     0.0326    0.02939   0.009887         45        640: 1\n",
      "tensor([0.60889], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.775      0.766      0.814      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.22G    0.03229    0.02888   0.009843         36        640: 1\n",
      "tensor([0.46089], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.775      0.761      0.812       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.22G    0.03202    0.02841   0.009371         20        640: 1\n",
      "tensor([0.47578], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.756      0.815      0.563\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.22G    0.03163    0.02836   0.009357         25        640: 1\n",
      "tensor([0.41687], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774      0.769      0.815      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.22G    0.03142    0.02834   0.009136         34        640: 1\n",
      "tensor([0.47941], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.761      0.815      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.22G    0.03137    0.02824    0.00891         47        640: 1\n",
      "tensor([0.50599], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.781      0.765      0.818      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.22G    0.03084    0.02776   0.008736         35        640: 1\n",
      "tensor([0.42947], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.776       0.82      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.22G    0.03066    0.02802   0.008507         41        640: 1\n",
      "tensor([0.42199], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771      0.783      0.824      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.22G    0.03054     0.0277   0.008215         41        640: 1\n",
      "tensor([0.61190], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.765      0.821      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.22G    0.03018    0.02752   0.008145         23        640: 1\n",
      "tensor([0.49683], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.792      0.769      0.824      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.22G    0.02981    0.02713    0.00788         29        640: 1\n",
      "tensor([0.48870], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.793       0.77      0.824      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.22G    0.02953    0.02718   0.007682         33        640: 1\n",
      "tensor([0.44415], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782      0.779      0.825      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.22G    0.02921    0.02672   0.007555         33        640: 1\n",
      "tensor([0.44443], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.787      0.777      0.826      0.581\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.22G     0.0291    0.02654   0.007427         34        640: 1\n",
      "tensor([0.45969], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.777      0.826      0.583\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.22G    0.02889    0.02644   0.007275         35        640: 1\n",
      "tensor([0.57186], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.781      0.828      0.585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.22G    0.02865    0.02647   0.007147         33        640: 1\n",
      "tensor([0.38577], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.792      0.776      0.828      0.585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.22G    0.02819    0.02598    0.00693         34        640: 1\n",
      "tensor([0.49738], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.79      0.776      0.828      0.586\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.22G    0.02788    0.02581   0.006707         40        640: 1\n",
      "tensor([0.61295], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.791      0.782       0.83      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.22G    0.02785    0.02576   0.006524         42        640: 1\n",
      "tensor([0.36117], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.794      0.779       0.83      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.22G    0.02747    0.02563    0.00653         26        640: 1\n",
      "tensor([0.24769], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.796      0.779      0.831      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.22G    0.02723    0.02568   0.006423         21        640: 1\n",
      "tensor([0.33173], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.795      0.783      0.831      0.587\n",
      "\n",
      "50 epochs completed in 1.803 hours.\n",
      "Optimizer stripped from runs/train/baseline_VOC/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/baseline_VOC/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/baseline_VOC/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.797      0.779      0.831      0.587\n",
      "                   car       4952       1201      0.826      0.907      0.928      0.716\n",
      "                person       4952       4528      0.857       0.81      0.889      0.601\n",
      "             aeroplane       4952        285      0.931      0.825      0.911      0.626\n",
      "               bicycle       4952        337      0.914      0.853      0.918      0.658\n",
      "                  bird       4952        459      0.804      0.767      0.821      0.534\n",
      "                  boat       4952        263      0.705        0.7      0.744      0.454\n",
      "                bottle       4952        469      0.712      0.765      0.783      0.533\n",
      "                   bus       4952        213      0.868      0.864      0.905      0.749\n",
      "                   cat       4952        358      0.864      0.835      0.852      0.637\n",
      "                 chair       4952        756      0.636      0.636      0.674      0.449\n",
      "                   cow       4952        244      0.752      0.844       0.87      0.647\n",
      "           diningtable       4952        206      0.763       0.68      0.767      0.539\n",
      "                   dog       4952        489       0.86      0.751      0.868      0.629\n",
      "                 horse       4952        348      0.852      0.863       0.91      0.658\n",
      "             motorbike       4952        325      0.843      0.823      0.893       0.59\n",
      "           pottedplant       4952        480      0.675      0.554      0.591      0.315\n",
      "                 sheep       4952        242      0.752      0.813      0.851      0.625\n",
      "                  sofa       4952        239      0.678      0.703      0.746      0.575\n",
      "                 train       4952        282      0.851      0.808      0.868       0.61\n",
      "             tvmonitor       4952        308      0.793      0.779      0.828      0.603\n",
      "Results saved to \u001b[1mruns/train/baseline_VOC\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : baseline_VOC\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/09a757b84d9d4072b3b811748eb74e22\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8744411626379467\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 17.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.9114995966291118\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.6264012505054095\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.930744180634387\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.8245614035087719\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 235.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8824612337110125\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 27.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.9181946790509589\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6580961867513487\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.9141305545764145\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.852912754495346\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 287.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.7846644246842118\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 86.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.8213883442119894\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.5335397009395363\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.8035953092576187\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.7666049491757553\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 352.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.7021505415763318\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 77.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.7444862736373566\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.45374950396656644\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.704699687121962\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6996197718631179\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 184.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.73787768660125\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 145.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7834892916202647\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.533216576186285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.7122811986024685\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.7653824162945224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 359.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8661259138635878\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 28.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.9048897855503477\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7490712889234333\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8684140889571752\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.863849765258216\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 184.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8646844707932353\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 229.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9282558909679646\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.7160532023256421\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8262681634194747\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.9068471999362924\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1089.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.8491121511208077\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 47.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8515077231159056\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.6365937599973581\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8635004074499224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.835195530726257\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 299.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6361006450915879\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 275.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6743845091625569\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.44868737304440787\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6359579679733552\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.6362433862433863\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 481.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.795210977591138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 68.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8702740528399847\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6468836591913465\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7515464172564136\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8442622950819673\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 206.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.7189102041922477\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 43.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7666231142066644\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.5392868853354535\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7630325740328117\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6796116504854369\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 140.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.8017787360186961\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 60.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.86830648218861\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.6291003441190799\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.8596024289298381\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.7512440866224097\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 367.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8575464833475072\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 52.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.909623401561328\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6578021400665417\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8523762522883811\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8627798189845869\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 300.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (0.5976940393447876, 4.090961456298828)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.5591500061569075, 0.8309250580424727)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.3060061105720733, 0.5874863705399849)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.5790389156291698, 0.7962177866261005)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.5376886267417876, 0.7832115348144233)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.8327497392345385\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 50.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8927087887223216\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5898828764583329\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8425370691406443\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.8231871871871872\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 268.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8327585522917788\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 612.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8887515282542268\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.6007295912857125\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8569789227562574\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8098696090083015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3667.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.608732983316593\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 128.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5908289240886724\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.3152069372098777\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6752187588405583\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.5541666666666667\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 266.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7811649155241602\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 65.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8508820538956456\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.6245859112439678\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7516801248777499\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8130572345448378\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 197.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6900940410573637\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 80.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.7458586509320303\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.5747920882898819\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6777195113358101\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.702928870292887\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 168.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.0272311232984066, 0.05995671823620796)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.0064231944270431995, 0.04811222106218338)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.025625411421060562, 0.035226110368967056)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.8287561839274149\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 40.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8678058773955915\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.6097785031892003\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8506584377231143\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.8079534724924795\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 228.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.786223728059866\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 63.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.8276873777077701\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.6029375730124072\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7933536910405936\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7792207792207793\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 240.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.029293663799762726, 0.04024447500705719)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.005433609243482351, 0.016682574525475502)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.017376087605953217, 0.021835066378116608)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : baseline_VOC\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/09a757b84d9d4072b3b811748eb74e22\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/baseline_VOC\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.07 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights yolov5s.pt \\\n",
    "--name baseline_VOC \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ac7325c-9239-4aa1-90c9-f3d343cee013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTI.yaml, weights=['runs/train/baseline_VOC/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 b2fc1a21 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.795      0.783      0.831      0.587\n",
      "                   car       4952       1201      0.824      0.909      0.929      0.717\n",
      "                person       4952       4528      0.855      0.813      0.889      0.601\n",
      "             aeroplane       4952        285      0.933      0.835      0.911      0.626\n",
      "               bicycle       4952        337      0.915      0.852      0.918      0.658\n",
      "                  bird       4952        459      0.802       0.77      0.821      0.535\n",
      "                  boat       4952        263      0.707      0.711      0.746      0.453\n",
      "                bottle       4952        469        0.7      0.768      0.783      0.532\n",
      "                   bus       4952        213      0.864      0.859      0.904      0.749\n",
      "                   cat       4952        358      0.866      0.832       0.85      0.635\n",
      "                 chair       4952        756      0.624      0.639      0.673      0.449\n",
      "                   cow       4952        244      0.746       0.84      0.868      0.646\n",
      "           diningtable       4952        206      0.753      0.689      0.773      0.542\n",
      "                   dog       4952        489      0.859      0.753       0.87       0.63\n",
      "                 horse       4952        348      0.859      0.865       0.91      0.659\n",
      "             motorbike       4952        325      0.847      0.821      0.895      0.589\n",
      "           pottedplant       4952        480      0.668       0.55      0.586      0.314\n",
      "                 sheep       4952        242      0.754      0.826      0.852      0.622\n",
      "                  sofa       4952        239      0.686      0.715      0.744      0.573\n",
      "                 train       4952        282      0.853      0.821      0.868      0.614\n",
      "             tvmonitor       4952        308      0.785      0.784      0.827        0.6\n",
      "Speed: 0.1ms pre-process, 2.0ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp320\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/baseline_VOC/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6afddfbf-7cd9-4d37-b78f-d7da2a7bbb53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/baseline_VOC/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 b2fc1a21 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.177      0.141       0.15     0.0779\n",
      "                   car       2244       8711       0.81      0.653      0.724      0.397\n",
      "                   van       2244        861          0          0          0          0\n",
      "                 truck       2244        333          0          0          0          0\n",
      "                  tram       2244        138          0          0          0          0\n",
      "                person       2244       1286       0.61      0.478      0.474      0.226\n",
      "        person_sitting       2244         89          0          0          0          0\n",
      "               cyclist       2244        496          0          0          0          0\n",
      "                  misc       2244        284          0          0          0          0\n",
      "Speed: 0.0ms pre-process, 1.1ms inference, 1.3ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp321\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/baseline_VOC/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0870acf2-b421-469d-8043-d1fd28ba6273",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71084887-c943-4d22-8a83-e5e417b39e3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "556cad09-1019-4497-882e-ce22533c21ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f0b997c7-c900-4eb7-a410-332936b52237",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_plain, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': Error in the HTTP2 framing layer\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/f4c15ffbaaa4415787b7a3ea14c850bb\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83607  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 214 layers, 7089751 parameters, 7089751 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 16551 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_plain/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_plain\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.64G    0.06632    0.04336    0.06443         36        640: 1\n",
      "tensor([0.95060], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.577      0.147      0.114     0.0554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      6.22G    0.04971    0.03585    0.04231         58        640: 1\n",
      "tensor([1.16301], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.422      0.448      0.381      0.191\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      6.22G    0.04794    0.03577    0.02993         37        640: 1\n",
      "tensor([0.71998], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.519      0.469      0.477      0.243\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      6.22G    0.04671    0.03676    0.02844         46        640: 1\n",
      "tensor([0.85035], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.607      0.518      0.561      0.303\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      6.22G    0.04507     0.0362    0.02575         39        640: 1\n",
      "tensor([0.85312], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.577       0.55      0.551      0.299\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      6.22G    0.04338    0.03562    0.02323         28        640: 1\n",
      "tensor([0.64450], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.626       0.58      0.611      0.346\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      6.22G    0.04239    0.03525    0.02176         39        640: 1\n",
      "tensor([0.69713], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.616      0.588      0.615      0.356\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      6.22G    0.04148    0.03473    0.02102         34        640: 1\n",
      "tensor([0.66143], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.658      0.607      0.649      0.378\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      6.22G    0.04081    0.03426    0.01992         41        640: 1\n",
      "tensor([0.58418], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.659      0.622      0.669       0.39\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      6.22G    0.03997    0.03337    0.01912         46        640: 1\n",
      "tensor([0.62025], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.693       0.64      0.692      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      6.22G    0.03973    0.03371    0.01859         30        640: 1\n",
      "tensor([0.61287], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.685      0.655      0.701      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      6.22G    0.03916     0.0337    0.01779         26        640: 1\n",
      "tensor([0.55372], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.707      0.659      0.708      0.436\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      6.22G    0.03869    0.03336    0.01735         33        640: 1\n",
      "tensor([0.64992], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.693      0.675      0.711      0.432\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      6.22G    0.03843    0.03315    0.01688         30        640: 1\n",
      "tensor([0.66260], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.714      0.661      0.715      0.443\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      6.22G    0.03838    0.03284    0.01628         33        640: 1\n",
      "tensor([0.63970], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.691      0.736      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      6.22G    0.03772    0.03266    0.01609         23        640: 1\n",
      "tensor([0.51084], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.721      0.686       0.74      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      6.22G    0.03743    0.03235    0.01568         40        640: 1\n",
      "tensor([0.63251], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.724      0.688      0.744      0.474\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      6.22G    0.03698    0.03233    0.01535         45        640: 1\n",
      "tensor([0.56573], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.725      0.686      0.743      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      6.22G    0.03702    0.03228    0.01496         20        640: 1\n",
      "tensor([0.36642], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.732      0.698      0.755      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      6.22G    0.03669    0.03218    0.01464         38        640: 1\n",
      "tensor([0.60721], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.731      0.693      0.752      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      6.22G    0.03648    0.03174    0.01434         33        640: 1\n",
      "tensor([0.60599], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.751      0.704      0.769        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      6.22G    0.03631    0.03176    0.01413         28        640: 1\n",
      "tensor([0.44323], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.752      0.708      0.768      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      6.22G    0.03612    0.03184    0.01369         27        640: 1\n",
      "tensor([0.54225], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.718      0.772      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      6.22G     0.0359    0.03121    0.01356         29        640: 1\n",
      "tensor([0.65914], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.753      0.722       0.78      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      6.22G    0.03579    0.03132    0.01317         35        640: 1\n",
      "tensor([0.46052], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.756      0.724      0.781      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      6.22G    0.03556    0.03113    0.01341         31        640: 1\n",
      "tensor([0.44891], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.734      0.786      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      6.22G    0.03555    0.03104    0.01321         40        640: 1\n",
      "tensor([0.68871], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.734      0.786      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      6.22G    0.03533    0.03128     0.0127         29        640: 1\n",
      "tensor([0.53326], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.761      0.733      0.788      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      6.22G     0.0352    0.03092    0.01263         26        640: 1\n",
      "tensor([0.45464], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.748       0.74      0.787      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      6.22G    0.03492    0.03105    0.01212         45        640: 1\n",
      "tensor([0.62406], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.736      0.795      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      6.22G    0.03475    0.03056    0.01221         36        640: 1\n",
      "tensor([0.48539], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.759      0.749      0.797      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      6.22G    0.03449    0.03016    0.01197         20        640: 1\n",
      "tensor([0.55131], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.747      0.797      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      6.22G    0.03442    0.03033    0.01213         25        640: 1\n",
      "tensor([0.44686], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.741      0.797       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      6.22G    0.03433     0.0304      0.012         34        640: 1\n",
      "tensor([0.51264], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.742      0.802      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      6.22G     0.0344    0.03043    0.01179         47        640: 1\n",
      "tensor([0.57493], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.748      0.804      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      6.22G      0.034    0.02997    0.01177         35        640: 1\n",
      "tensor([0.49966], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.755      0.807      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      6.22G    0.03385    0.03039    0.01139         41        640: 1\n",
      "tensor([0.53775], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.759      0.807      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      6.22G    0.03393     0.0301    0.01118         41        640: 1\n",
      "tensor([0.69680], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.763      0.809      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      6.22G    0.03371    0.03011     0.0113         23        640: 1\n",
      "tensor([0.55599], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.753       0.81      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      6.22G    0.03338    0.02964    0.01096         29        640: 1\n",
      "tensor([0.55532], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.763       0.81      0.557\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      6.22G    0.03327    0.02988    0.01078         33        640: 1\n",
      "tensor([0.53416], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.758      0.812      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      6.22G    0.03305    0.02954    0.01076         33        640: 1\n",
      "tensor([0.51409], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.765      0.814      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      6.22G    0.03322    0.02951    0.01089         34        640: 1\n",
      "tensor([0.66209], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.775      0.764      0.813      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      6.22G    0.03315     0.0295    0.01077         35        640: 1\n",
      "tensor([0.58965], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.777      0.762      0.815      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      6.22G    0.03302     0.0296    0.01074         33        640: 1\n",
      "tensor([0.44561], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.766      0.817      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      6.22G    0.03269    0.02922    0.01041         34        640: 1\n",
      "tensor([0.55335], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.768      0.817      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      6.22G    0.03253    0.02905    0.01014         40        640: 1\n",
      "tensor([0.61573], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.772      0.818      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      6.22G    0.03264    0.02914    0.01017         42        640: 1\n",
      "tensor([0.44004], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.779      0.768      0.819      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      6.22G    0.03236    0.02911    0.01013         26        640: 1\n",
      "tensor([0.28462], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.779      0.766       0.82      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      6.22G    0.03217    0.02913   0.009959         21        640: 1\n",
      "tensor([0.38431], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.765       0.82      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      6.22G    0.03232    0.02889   0.009761         32        640: 1\n",
      "tensor([0.41720], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.766       0.82      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      6.22G    0.03203    0.02908   0.009687         95        640:  ^C\n",
      "      51/99      6.22G    0.03203    0.02908   0.009687         95        640:  \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name increment_VOC_plain \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0\n",
    "# 16:34创建18:13训完\n",
    "# 1 + (26 + 13)/60 = 1.65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3113c8a0-468a-4834-bd44-c9cfc20e67ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTI.yaml, weights=['runs/train/increment_VOC_plain/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783      0.766      0.821      0.569\n",
      "                   car       4952       1201      0.812      0.899      0.924      0.709\n",
      "                person       4952       4528      0.846      0.806      0.881      0.583\n",
      "             aeroplane       4952        285      0.907      0.821      0.882      0.594\n",
      "               bicycle       4952        337       0.91      0.813      0.904      0.644\n",
      "                  bird       4952        459        0.8      0.732      0.796      0.516\n",
      "                  boat       4952        263      0.699      0.673      0.731      0.424\n",
      "                bottle       4952        469      0.734      0.729      0.778      0.527\n",
      "                   bus       4952        213      0.835       0.84      0.887      0.731\n",
      "                   cat       4952        358       0.86      0.813      0.854      0.619\n",
      "                 chair       4952        756      0.624      0.618      0.662      0.437\n",
      "                   cow       4952        244      0.742      0.849      0.856      0.625\n",
      "           diningtable       4952        206       0.77      0.684      0.756      0.518\n",
      "                   dog       4952        489      0.834      0.736       0.85      0.586\n",
      "                 horse       4952        348      0.855      0.844      0.897       0.63\n",
      "             motorbike       4952        325      0.828      0.787      0.878      0.573\n",
      "           pottedplant       4952        480      0.638      0.557       0.61      0.333\n",
      "                 sheep       4952        242      0.721      0.821      0.841       0.61\n",
      "                  sofa       4952        239      0.672      0.699      0.736       0.54\n",
      "                 train       4952        282      0.847      0.812      0.874      0.603\n",
      "             tvmonitor       4952        308      0.735      0.792      0.817      0.585\n",
      "Speed: 0.1ms pre-process, 1.6ms inference, 1.1ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp196\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/increment_VOC_plain/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0cc4853-25d9-459c-8fab-230309523f2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7520f302-c483-4f82-a826-0fd73e092350",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_plain/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.926      0.142      0.163     0.0873\n",
      "                   car       2244       8711      0.818      0.642      0.724      0.399\n",
      "                   van       2244        861          1          0     0.0709     0.0489\n",
      "                 truck       2244        333          1          0     0.0189     0.0158\n",
      "                  tram       2244        138          1          0    0.00252    0.00126\n",
      "                person       2244       1286      0.586      0.495      0.478      0.227\n",
      "        person_sitting       2244         89          1          0          0          0\n",
      "               cyclist       2244        496          1          0    0.00214   0.000533\n",
      "                  misc       2244        284          1          0    0.00914    0.00607\n",
      "Speed: 0.0ms pre-process, 0.7ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp142\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/increment_VOC_plain/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "571f0f34-fb02-44a8-9572-ea16fed60c24",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e2b8607-edde-48e5-af1f-85ea8313d691",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d49cca6-67e4-4bc0-9572-2b65984a6b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SI和ewc等方法并不使用于CIL。 iCaRL, DER++, LwF, GDumb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24656a42-0df2-4d3c-aeed-ddf5fc85dc5e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_SI, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=True, SI_pt=./runs/train/fog_02/weights/si.pt, SI_lambda=0.1\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/f7b8611e9c7f48beac640d025b9dedc3\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83607  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 214 layers, 7089751 parameters, 7089751 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 16551 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_SI/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_SI\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       3.6G    0.06632    0.04336    0.06444         36        640: 1\n",
      "tensor([0.95417], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00118], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.577      0.146      0.113     0.0544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.17G    0.04964    0.03587    0.04233         58        640: 1\n",
      "tensor([1.17367], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00270], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.409      0.449       0.38      0.193\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.17G    0.04793    0.03572    0.03008         37        640: 1\n",
      "tensor([0.78961], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.00640], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.503       0.52      0.506      0.263\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.17G    0.04634    0.03658    0.02821         46        640: 1\n",
      "tensor([0.76632], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.01577], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.57      0.523      0.543      0.295\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.17G    0.04498    0.03615    0.02551         39        640: 1\n",
      "tensor([0.79552], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.02480], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.608      0.538      0.562      0.301\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.17G    0.04319    0.03556    0.02295         28        640: 1\n",
      "tensor([0.67755], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.03156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.612      0.569      0.594      0.339\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.17G    0.04222    0.03515    0.02164         39        640: 1\n",
      "tensor([0.72903], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.03632], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.634      0.604      0.638       0.37\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.17G    0.04143    0.03471    0.02102         34        640: 1\n",
      "tensor([0.63225], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04048], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.665      0.614      0.651      0.384\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.17G    0.04083    0.03427    0.02005         41        640: 1\n",
      "tensor([0.67137], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04382], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.666      0.634      0.671      0.394\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.17G    0.03998    0.03343    0.01914         46        640: 1\n",
      "tensor([0.69473], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04630], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.694      0.627      0.687      0.407\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.17G    0.03957    0.03362    0.01852         30        640: 1\n",
      "tensor([0.57662], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04837], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.694      0.658      0.709      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.17G    0.03896     0.0336    0.01796         26        640: 1\n",
      "tensor([0.52204], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04958], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.695      0.657      0.705      0.432\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.17G    0.03846    0.03325    0.01708         33        640: 1\n",
      "tensor([0.69085], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05069], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.699      0.676      0.719      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.17G    0.03822    0.03294    0.01661         30        640: 1\n",
      "tensor([0.64670], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05156], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.712      0.678      0.729      0.461\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.17G    0.03812     0.0327    0.01627         33        640: 1\n",
      "tensor([0.75098], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05234], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.73      0.674      0.735      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.17G    0.03739    0.03251    0.01579         23        640: 1\n",
      "tensor([0.53202], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05298], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.702      0.686      0.732      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.17G    0.03716    0.03236    0.01588         40        640: 1\n",
      "tensor([0.68174], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05352], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.738      0.687      0.754      0.479\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.17G    0.03671    0.03213    0.01501         45        640: 1\n",
      "tensor([0.59106], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05369], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.719      0.703      0.747      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.17G    0.03657    0.03211    0.01472         20        640: 1\n",
      "tensor([0.40443], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05398], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.735      0.704      0.759      0.489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.17G    0.03613    0.03196     0.0142         38        640: 1\n",
      "tensor([0.71497], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05407], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.743      0.709      0.767      0.499\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.17G    0.03597    0.03143    0.01374         33        640: 1\n",
      "tensor([0.63816], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05406], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.713      0.777      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.17G    0.03556    0.03139    0.01352         28        640: 1\n",
      "tensor([0.47751], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05376], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.735      0.724      0.774      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.17G    0.03528    0.03136     0.0129         27        640: 1\n",
      "tensor([0.54989], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05362], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.742      0.724      0.776      0.512\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.17G    0.03517    0.03079    0.01306         29        640: 1\n",
      "tensor([0.68821], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05356], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.756      0.714       0.78      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.17G    0.03486    0.03083    0.01236         35        640: 1\n",
      "tensor([0.49039], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05317], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.776      0.721       0.79      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.17G    0.03458    0.03064    0.01254         31        640: 1\n",
      "tensor([0.51123], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05306], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.743      0.791      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.17G    0.03462     0.0305    0.01258         40        640: 1\n",
      "tensor([0.76063], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05283], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.727      0.788      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.17G    0.03405    0.03068    0.01161         29        640: 1\n",
      "tensor([0.51031], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05231], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774      0.735      0.799      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.17G    0.03399     0.0303    0.01162         26        640: 1\n",
      "tensor([0.45164], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05198], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.738      0.798      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.17G    0.03362    0.03031    0.01112         45        640: 1\n",
      "tensor([0.65461], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05147], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.743        0.8      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.17G    0.03323    0.02979    0.01109         36        640: 1\n",
      "tensor([0.53497], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05098], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.753      0.802      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.17G      0.033    0.02927    0.01073         20        640: 1\n",
      "tensor([0.55186], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05063], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.759      0.756      0.803      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.17G    0.03251    0.02926    0.01049         25        640: 1\n",
      "tensor([0.49069], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.05006], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.751      0.804      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.17G     0.0323     0.0292    0.01029         34        640: 1\n",
      "tensor([0.53907], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04947], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.754      0.807      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.17G    0.03224    0.02909    0.00997         47        640: 1\n",
      "tensor([0.60516], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04892], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.776      0.756      0.807      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.17G    0.03168     0.0286   0.009797         35        640: 1\n",
      "tensor([0.49059], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04839], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764       0.77       0.81      0.557\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.17G    0.03148    0.02887    0.00958         41        640: 1\n",
      "tensor([0.52351], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04783], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.762      0.813       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.17G    0.03152    0.02861     0.0092         41        640: 1\n",
      "tensor([0.67312], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04728], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.759      0.814      0.563\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.17G    0.03111    0.02847   0.009147         23        640: 1\n",
      "tensor([0.54988], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04679], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782      0.763      0.817      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.17G    0.03069    0.02802   0.008848         29        640: 1\n",
      "tensor([0.55047], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04627], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.79      0.753      0.817      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.17G    0.03038    0.02802   0.008512         33        640: 1\n",
      "tensor([0.48809], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04580], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.785      0.761      0.818      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.17G    0.03003    0.02754   0.008319         33        640: 1\n",
      "tensor([0.51581], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04532], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782      0.761      0.818      0.569\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.17G    0.02988    0.02737   0.008314         34        640: 1\n",
      "tensor([0.58069], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04483], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.776       0.77      0.818       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.17G    0.02959    0.02722   0.008088         35        640: 1\n",
      "tensor([0.56232], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04443], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.787      0.764       0.82      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.17G    0.02941    0.02725   0.007822         33        640: 1\n",
      "tensor([0.42419], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04403], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.791      0.764      0.822      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.17G    0.02881    0.02666   0.007642         34        640: 1\n",
      "tensor([0.52788], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04362], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.792      0.768      0.822      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.17G    0.02849     0.0265   0.007357         40        640: 1\n",
      "tensor([0.62797], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04328], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.793      0.764      0.822      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.17G    0.02846    0.02647   0.007137         42        640: 1\n",
      "tensor([0.41180], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04295], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.768      0.822      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.17G    0.02802    0.02634   0.007124         26        640: 1\n",
      "tensor([0.28554], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04269], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.781      0.776      0.822      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.17G    0.02779    0.02633    0.00696         21        640: 1\n",
      "tensor([0.36687], device='cuda:0', grad_fn=<AddBackward0>) tensor([0.04247], device='cuda:0', grad_fn=<DivBackward0>)\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.774      0.823      0.577\n",
      "\n",
      "50 epochs completed in 2.210 hours.\n",
      "Optimizer stripped from runs/train/increment_VOC_SI/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/increment_VOC_SI/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/increment_VOC_SI/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.776      0.823      0.577\n",
      "                   car       4952       1201       0.82      0.895      0.925       0.72\n",
      "                person       4952       4528      0.859      0.819       0.89      0.596\n",
      "             aeroplane       4952        285      0.906      0.828      0.891      0.607\n",
      "               bicycle       4952        337      0.881      0.825       0.91       0.64\n",
      "                  bird       4952        459        0.8      0.739       0.79      0.521\n",
      "                  boat       4952        263      0.658       0.68      0.716      0.425\n",
      "                bottle       4952        469      0.688       0.74      0.764      0.518\n",
      "                   bus       4952        213       0.83      0.854      0.895      0.733\n",
      "                   cat       4952        358      0.843      0.821       0.87       0.65\n",
      "                 chair       4952        756       0.64      0.638      0.682      0.448\n",
      "                   cow       4952        244      0.778      0.864      0.868      0.636\n",
      "           diningtable       4952        206      0.744      0.709      0.769       0.53\n",
      "                   dog       4952        489       0.83      0.747      0.842      0.595\n",
      "                 horse       4952        348      0.868      0.868      0.911      0.658\n",
      "             motorbike       4952        325      0.847      0.803       0.89      0.582\n",
      "           pottedplant       4952        480      0.629      0.536      0.564      0.306\n",
      "                 sheep       4952        242      0.733      0.826      0.826      0.608\n",
      "                  sofa       4952        239      0.676      0.715      0.758      0.567\n",
      "                 train       4952        282      0.861      0.809       0.87      0.611\n",
      "             tvmonitor       4952        308      0.781      0.799      0.823      0.593\n",
      "Results saved to \u001b[1mruns/train/increment_VOC_SI\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m The process of logging environment details (conda environment, git patch) is underway. Please be patient as this may take some time.\n",
      "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Failed to complete logging of all environment details (conda environment, git patch)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : increment_VOC_SI\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/f7b8611e9c7f48beac640d025b9dedc3\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8652649131224323\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 24.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8911598753129175\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.6071317526580008\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9059581752143517\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.8280701754385965\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 236.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8519086790704731\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 38.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.9095179713632653\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6395725962336856\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.880716421691574\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.8249258160237388\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 278.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.768065267704086\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 85.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.7901357137137108\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.5210467038272297\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7996055287093023\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.7389187818381718\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 339.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6686111439645239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 93.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.7161781850815276\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.42534864884640944\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6578107515404021\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6797721132321893\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 179.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.7128939213378338\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 157.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7639953548867451\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.5178622016993776\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6878139819320604\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.7398720682302772\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 347.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8422249611753387\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 37.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8946938285664139\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7327390650168942\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8303352750841334\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8544600938967136\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 182.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8560693218597374\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 236.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9247037857133293\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.7201237823109892\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8200382596149651\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8954121839217594\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1075.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.8320013004376877\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 55.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.870441972009286\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.6496908923827703\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8430599110126823\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.8212290502793296\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 294.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6386082856081698\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 272.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6817806895877282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.4476999655114076\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6396538461611503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.6375661375661376\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 482.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.8189969982493761\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 60.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8676492645759688\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6359529533852872\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7784475208782785\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8640030807830354\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 211.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.7260921235974974\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 50.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7685291813389008\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.5302947004590993\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7443175936045164\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.7087378640776699\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 146.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7860070009242033\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 75.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.8418958703544889\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.5947568719877193\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.8296093082248286\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.7467591047837961\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 365.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8679124627030612\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 46.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.9111872063207077\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6576560584894435\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8680088548583695\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.867816091954023\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 302.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (0.6869037747383118, 3.8577706813812256)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.11318434022799626, 0.8226623799073387)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.05443776546918992, 0.5771236181575978)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.40871115588049467, 0.7928958125642337)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.14597051094005886, 0.7762618726466537)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.824465792424818\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 47.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8900761268335423\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5823972768239493\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8473547696878315\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.8027808577808577\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 261.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8382741572504432\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 610.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8897770493169879\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5964864065221682\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8587132380907178\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8187854373808439\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3707.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5789556631300452\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 152.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5637583346701995\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.30585305810442315\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6288031287693363\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.5364308753197643\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 257.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7766743173377287\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 73.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8261086637998407\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.6077927947277361\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7325567631441805\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8264462809917356\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 200.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6951538378842969\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 82.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.7576848195853589\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.5667304606159842\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6759496238662905\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.7154811715481172\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 171.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.027787314727902412, 0.0663197934627533)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.006959531921893358, 0.06444112956523895)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.02633085288107395, 0.043362393975257874)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.833720555178644\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 37.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8696330437428376\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.610805763648517\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.860553194704138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.8085106382978723\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 228.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7896965023986443\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 69.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.8228909155501187\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.592595231868904\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7808924879322392\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7987012987012987\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 246.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.029606441035866737, 0.04660564288496971)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.0052854628302156925, 0.04501483216881752)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.01751868613064289, 0.023068377748131752)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : increment_VOC_SI\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/f7b8611e9c7f48beac640d025b9dedc3\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/increment_VOC_SI\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.05 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Failed to log run in comet.com\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--SI_enable \\\n",
    "--SI_pt ./runs/train/fog_02/weights/si.pt \\\n",
    "--SI_lambda 1e-1 \\\n",
    "--name increment_VOC_SI \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "233f1e6b-0f2d-46be-88b3-c8d324f78d25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_SI/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.173      0.151      0.156      0.083\n",
      "                   car       2244       8711      0.812      0.683      0.757       0.43\n",
      "                   van       2244        861          0          0          0          0\n",
      "                 truck       2244        333          0          0          0          0\n",
      "                  tram       2244        138          0          0          0          0\n",
      "                person       2244       1286      0.575      0.524       0.49      0.234\n",
      "        person_sitting       2244         89          0          0          0          0\n",
      "               cyclist       2244        496          0          0          0          0\n",
      "                  misc       2244        284          0          0          0          0\n",
      "Speed: 0.1ms pre-process, 0.9ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp143\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/increment_VOC_SI/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "495147c1-34b4-4bc4-a65f-2338edda558e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e70d887-1daf-4d95-955e-770da9a4eaaf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_ewc, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=runs/train/fog_02/weights/fisher.pt, ewc_lambda=0.001, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/2181c0674e1d4093aa9b9ec6dd82a1ca\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83607  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 214 layers, 7089751 parameters, 7089751 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007... 16551 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/train2007.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_ewc/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_ewc\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.52G    0.06635    0.04336    0.06444         36        640: 1\n",
      "tensor([0.95688], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.576      0.149      0.113     0.0548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.09G    0.04964    0.03588    0.04231         58        640: 1\n",
      "tensor([1.17116], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.405      0.445      0.373      0.189\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.09G     0.0478    0.03573    0.03004         37        640: 1\n",
      "tensor([0.78584], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.472      0.446      0.419      0.211\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.09G    0.04649     0.0366    0.02816         46        640: 1\n",
      "tensor([0.74056], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.579      0.525      0.548      0.293\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.09G     0.0449    0.03616    0.02563         39        640: 1\n",
      "tensor([0.81953], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.621      0.559      0.589      0.324\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.09G    0.04329     0.0356    0.02316         28        640: 1\n",
      "tensor([0.61569], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.619      0.578      0.614      0.347\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.09G    0.04216     0.0352    0.02153         39        640: 1\n",
      "tensor([0.73647], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.663      0.599      0.638      0.368\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.09G    0.04128    0.03463     0.0208         34        640: 1\n",
      "tensor([0.65769], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.685      0.608      0.666      0.387\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.09G    0.04066    0.03427     0.0199         41        640: 1\n",
      "tensor([0.64988], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.674      0.609      0.661      0.393\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.09G    0.03984    0.03337    0.01906         46        640: 1\n",
      "tensor([0.71533], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.696      0.645      0.702       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.09G    0.03936    0.03354    0.01835         30        640: 1\n",
      "tensor([0.57950], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.715      0.647       0.71      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.09G    0.03897    0.03355    0.01774         26        640: 1\n",
      "tensor([0.57152], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.713      0.667      0.717      0.443\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.09G    0.03815    0.03311    0.01666         33        640: 1\n",
      "tensor([0.66967], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.716      0.666      0.724      0.447\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.09G    0.03803     0.0329    0.01645         30        640: 1\n",
      "tensor([0.59777], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.718      0.673      0.726      0.453\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.09G     0.0378    0.03252    0.01576         33        640: 1\n",
      "tensor([0.66926], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.737      0.681      0.742      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.09G    0.03706    0.03238    0.01553         23        640: 1\n",
      "tensor([0.46603], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.72      0.684      0.738       0.47\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.09G    0.03675    0.03205    0.01519         40        640: 1\n",
      "tensor([0.63553], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.741      0.687      0.756      0.486\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.09G    0.03645    0.03198    0.01472         45        640: 1\n",
      "tensor([0.57946], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.729      0.702      0.752      0.486\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.09G    0.03618    0.03184     0.0141         20        640: 1\n",
      "tensor([0.34604], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.714      0.766      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.09G    0.03572    0.03168    0.01361         38        640: 1\n",
      "tensor([0.60830], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.743       0.71      0.767      0.505\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.09G    0.03553    0.03121    0.01326         33        640: 1\n",
      "tensor([0.62601], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.722      0.773      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.09G     0.0353    0.03122     0.0132         28        640: 1\n",
      "tensor([0.43072], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.722      0.776       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.09G    0.03484    0.03109    0.01249         27        640: 1\n",
      "tensor([0.50224], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.731       0.78      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.09G    0.03469    0.03055     0.0129         29        640: 1\n",
      "tensor([0.72373], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.717      0.786      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.09G    0.03451    0.03066    0.01226         35        640: 1\n",
      "tensor([0.44591], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.736      0.792      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.09G    0.03413    0.03038    0.01213         31        640: 1\n",
      "tensor([0.44800], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.738      0.792      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.09G    0.03416    0.03021    0.01186         40        640: 1\n",
      "tensor([0.68540], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.736      0.794      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.09G    0.03371    0.03038     0.0113         29        640: 1\n",
      "tensor([0.48228], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771      0.738      0.798      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.09G    0.03353    0.02992     0.0111         26        640: 1\n",
      "tensor([0.39844], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774      0.734      0.795       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.09G    0.03324    0.03007    0.01088         45        640: 1\n",
      "tensor([0.64249], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.744      0.801      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.09G    0.03289    0.02954    0.01085         36        640: 1\n",
      "tensor([0.46166], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.781      0.741      0.803      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.09G     0.0325    0.02899    0.01011         20        640: 1\n",
      "tensor([0.51197], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.775      0.751      0.808      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.09G    0.03217    0.02906    0.01029         25        640: 1\n",
      "tensor([0.42755], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.757      0.807      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.09G    0.03197    0.02903   0.009921         34        640: 1\n",
      "tensor([0.47924], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.761      0.812      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.09G    0.03193    0.02891   0.009727         47        640: 1\n",
      "tensor([0.60138], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.764       0.81       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.09G    0.03139    0.02839   0.009543         35        640: 1\n",
      "tensor([0.44429], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.779      0.753      0.811       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.09G    0.03119     0.0287   0.009274         41        640: 1\n",
      "tensor([0.48665], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774      0.765      0.816      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.09G    0.03116    0.02841   0.009017         41        640: 1\n",
      "tensor([0.64829], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.761      0.817      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.09G    0.03084    0.02832   0.008894         23        640: 1\n",
      "tensor([0.52332], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782      0.764      0.818      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.09G     0.0304     0.0278   0.008645         29        640: 1\n",
      "tensor([0.50494], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.788      0.762      0.819      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.09G    0.03012    0.02785   0.008421         33        640: 1\n",
      "tensor([0.45359], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.79      0.763       0.82      0.572\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.09G    0.02976     0.0274   0.008196         33        640: 1\n",
      "tensor([0.46406], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.791      0.762      0.819      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.09G    0.02968    0.02721    0.00812         34        640: 1\n",
      "tensor([0.49896], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.761      0.819      0.574\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.09G    0.02941    0.02707    0.00794         35        640: 1\n",
      "tensor([0.50191], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.793      0.758       0.82      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.09G    0.02916    0.02707   0.007719         33        640: 1\n",
      "tensor([0.38439], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.798      0.756      0.821      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.09G     0.0287    0.02659   0.007535         34        640: 1\n",
      "tensor([0.47390], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.797      0.763      0.822      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.09G    0.02841     0.0264   0.007254         40        640: 1\n",
      "tensor([0.58700], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.8      0.759      0.822      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.09G    0.02833    0.02637   0.007069         42        640: 1\n",
      "tensor([0.38378], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.795      0.762      0.822      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.09G    0.02796    0.02626   0.007042         26        640: 1\n",
      "tensor([0.27214], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.798      0.759      0.822      0.578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.09G    0.02773    0.02625   0.006906         21        640: 1\n",
      "tensor([0.34230], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.798      0.761      0.822      0.578\n",
      "\n",
      "50 epochs completed in 2.200 hours.\n",
      "Optimizer stripped from runs/train/increment_VOC_ewc/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/increment_VOC_ewc/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/increment_VOC_ewc/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.8      0.758      0.822      0.579\n",
      "                   car       4952       1201      0.845       0.89      0.926      0.718\n",
      "                person       4952       4528      0.869      0.801      0.889       0.59\n",
      "             aeroplane       4952        285      0.923      0.802      0.892      0.615\n",
      "               bicycle       4952        337      0.891      0.834      0.912      0.642\n",
      "                  bird       4952        459      0.811      0.719      0.797      0.521\n",
      "                  boat       4952        263      0.675      0.654      0.708      0.436\n",
      "                bottle       4952        469      0.697      0.746      0.772      0.521\n",
      "                   bus       4952        213      0.855      0.826      0.898       0.74\n",
      "                   cat       4952        358       0.86      0.821       0.86      0.633\n",
      "                 chair       4952        756      0.669      0.624      0.667      0.446\n",
      "                   cow       4952        244      0.768      0.855      0.875      0.647\n",
      "           diningtable       4952        206      0.763      0.639      0.739      0.522\n",
      "                   dog       4952        489       0.85      0.716      0.854      0.604\n",
      "                 horse       4952        348      0.863      0.854      0.899      0.646\n",
      "             motorbike       4952        325      0.893      0.788      0.906       0.59\n",
      "           pottedplant       4952        480      0.679       0.54      0.591      0.325\n",
      "                 sheep       4952        242      0.722      0.806      0.823      0.605\n",
      "                  sofa       4952        239      0.693      0.679      0.743      0.565\n",
      "                 train       4952        282      0.881      0.809      0.873      0.618\n",
      "             tvmonitor       4952        308      0.798      0.763      0.811      0.589\n",
      "Results saved to \u001b[1mruns/train/increment_VOC_ewc\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : increment_VOC_ewc\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/2181c0674e1d4093aa9b9ec6dd82a1ca\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8583669837523273\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 19.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8917976235377195\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.6148995720516803\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9232538783614053\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.8020017678497211\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 229.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.86156547301606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 34.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.9123551716856685\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6423642413967988\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8912119539384961\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.8338278931750742\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 281.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.7622242934615241\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 77.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.7970003763724042\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.5211882389094422\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.8108431281705659\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.7191060886594656\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 330.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6641444288958304\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 83.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.7083945740358252\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.43635464697548343\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6746166155769368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6539923954372624\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 172.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.7206132808371072\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 152.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7719033105897151\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.5205142321828147\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6966632497422965\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.746268656716418\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 350.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8406286360329939\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 30.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.897676681947166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.739817175446096\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8554725402463976\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8262910798122066\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 176.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8666784136121545\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 196.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9264794518629456\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.7179021594230734\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8449758969331066\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8895251412945001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1068.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.839939348286109\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 48.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8601014795509622\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.6333891357176429\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.859522087592263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.8212290502793296\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 294.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6457560545616932\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 234.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.667185846777183\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.44576632896834606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6686950976318036\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.6243386243386243\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 472.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.8090302871732614\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 63.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8750799157293353\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6470923775433721\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.767994725108221\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8546986193844117\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 209.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6956234971377921\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 41.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7391779480971904\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.5219880143897991\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7626271392057682\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6394427130247263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 132.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7770063175841672\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 62.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.8539722919807274\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.6035291214329368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.8497340585499786\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.7157464212678937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 350.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8585128494468652\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 47.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.8992368880326189\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6463536230271123\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8634036366763026\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8536771582748595\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 297.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (0.6227890849113464, 3.857753276824951)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.11334273585478231, 0.8222728961366418)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.05483503259874182, 0.5785939119067657)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.40488227264254223, 0.800056486099575)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.14901232531120948, 0.7649540273153269)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.8371751540438053\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 31.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.9056432957329932\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5898171581589666\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8932917600835049\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.7876923076923077\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 256.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8339644937271413\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 545.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8892668845136686\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5904432633016599\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8694072370476665\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8012983168681049\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3628.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.6012452060386538\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 123.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5911330600585194\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.3253132084403457\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6788184234261356\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.5395833333333333\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 259.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7614371815804696\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 75.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8228618750173241\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.6045292942966138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7217161383742633\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8057851239669421\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 195.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6856324117523992\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 72.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.7434173832794639\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.564654240652572\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.692610886937128\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6787931585700065\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 162.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.02772563137114048, 0.06634776294231415)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.006906346417963505, 0.06444133073091507)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.026247508823871613, 0.04335664212703705)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.8432308515860689\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 31.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.872711142016228\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.6178753799622863\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.881066878075916\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.8085106382978723\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 228.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7801795493473497\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 59.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.8114619361267779\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5885194076600454\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7981647526307197\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.762987012987013\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 235.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.029542837291955948, 0.04707348346710205)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.005380202550441027, 0.044995177537202835)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.017543230205774307, 0.023924563080072403)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : increment_VOC_ewc\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/2181c0674e1d4093aa9b9ec6dd82a1ca\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/increment_VOC_ewc\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.03 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--ewc_pt runs/train/fog_02/weights/fisher.pt \\\n",
    "--ewc_lambda 1e-3 \\\n",
    "--name increment_VOC_ewc \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "483656d1-2f11-4624-b602-3aa37d478dc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_ewc/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.178      0.147      0.155     0.0819\n",
      "                   car       2244       8711      0.814       0.67      0.749      0.421\n",
      "                   van       2244        861          0          0          0          0\n",
      "                 truck       2244        333          0          0          0          0\n",
      "                  tram       2244        138          0          0          0          0\n",
      "                person       2244       1286      0.606      0.508      0.488      0.234\n",
      "        person_sitting       2244         89          0          0          0          0\n",
      "               cyclist       2244        496          0          0          0          0\n",
      "                  misc       2244        284          0          0          0          0\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 1.0ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp162\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/increment_VOC_ewc/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77f1bb59-412d-4150-8422-b66abfed2d9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d96b2e8e-d619-433c-a376-3cad6ca9d266",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dca9016-bb58-47c0-86be-657cfec1f5fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "120ac2e6-f827-423f-9c68-74890cb9df9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d06cda19-acd8-44cb-a296-05ce66996e0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a53a4977-d119-491e-9e99-41811153f787",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_Lwf: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_Lwf, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=0.0001, Lwf_temperature=1.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/0a838d5ecabe4d46829230bb1fd3c410\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83607  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 214 layers, 7089751 parameters, 7089751 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from runs/train/fog_02/weights/best.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007... 16551 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/train2007.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_Lwf/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_Lwf\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       3.5G      0.113    0.04218    0.08386         48        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/49       3.5G    0.06964    0.04516    0.06544         36        640: 1\n",
      "tensor([1.41871], device='cuda:0', grad_fn=<AddBackward0>) tensor(3689.90649, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.444      0.104     0.0897     0.0398\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.08G    0.05308    0.03937    0.05083         58        640: 1\n",
      "tensor([1.78926], device='cuda:0', grad_fn=<AddBackward0>) tensor(5229.22119, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.31      0.282       0.23      0.105\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.08G    0.04989      0.038    0.03907         37        640: 1\n",
      "tensor([1.57954], device='cuda:0', grad_fn=<AddBackward0>) tensor(7637.82861, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.399      0.433      0.377      0.181\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.08G    0.04698    0.03686    0.03226         46        640: 1\n",
      "tensor([1.72073], device='cuda:0', grad_fn=<AddBackward0>) tensor(8811.31641, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.531      0.488      0.496      0.252\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.08G    0.04509    0.03613    0.02814         39        640: 1\n",
      "tensor([1.70234], device='cuda:0', grad_fn=<AddBackward0>) tensor(8788.57715, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.596      0.522      0.552      0.291\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.08G    0.04342    0.03556    0.02553         28        640: 1\n",
      "tensor([1.62690], device='cuda:0', grad_fn=<AddBackward0>) tensor(10033.89648, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.611      0.573      0.595      0.329\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.08G    0.04246    0.03527    0.02394         39        640: 1\n",
      "tensor([1.69232], device='cuda:0', grad_fn=<AddBackward0>) tensor(9975.41895, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.62      0.576      0.602      0.343\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.08G     0.0415    0.03474    0.02317         34        640: 1\n",
      "tensor([1.77655], device='cuda:0', grad_fn=<AddBackward0>) tensor(11076.83008, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.64      0.594       0.63      0.358\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.08G    0.04089     0.0344    0.02229         41        640: 1\n",
      "tensor([1.46137], device='cuda:0', grad_fn=<AddBackward0>) tensor(8688.22168, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.652        0.6      0.636      0.364\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.08G     0.0401    0.03347    0.02114         46        640: 1\n",
      "tensor([1.50756], device='cuda:0', grad_fn=<AddBackward0>) tensor(8867.41699, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.674      0.616       0.66      0.386\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.08G    0.03968    0.03377    0.02055         30        640: 1\n",
      "tensor([1.44141], device='cuda:0', grad_fn=<AddBackward0>) tensor(8147.13672, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.671      0.623      0.667      0.394\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.08G     0.0391    0.03381     0.0199         26        640: 1\n",
      "tensor([1.50416], device='cuda:0', grad_fn=<AddBackward0>) tensor(9225.91016, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.676      0.637      0.675      0.402\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.08G    0.03856    0.03342    0.01906         33        640: 1\n",
      "tensor([1.54111], device='cuda:0', grad_fn=<AddBackward0>) tensor(8915.40234, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.676      0.623      0.665      0.398\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.08G    0.03832    0.03321    0.01871         30        640: 1\n",
      "tensor([1.51723], device='cuda:0', grad_fn=<AddBackward0>) tensor(9054.55664, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.674       0.64      0.679      0.409\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.08G    0.03809    0.03291    0.01819         33        640: 1\n",
      "tensor([1.60153], device='cuda:0', grad_fn=<AddBackward0>) tensor(9592.36328, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.698      0.653      0.699      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.08G    0.03751    0.03281    0.01791         23        640: 1\n",
      "tensor([1.32979], device='cuda:0', grad_fn=<AddBackward0>) tensor(7889.89209, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.664      0.656       0.69      0.423\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.08G    0.03714    0.03244    0.01751         40        640: 1\n",
      "tensor([1.60822], device='cuda:0', grad_fn=<AddBackward0>) tensor(9497.70898, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.704      0.659      0.711      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.08G    0.03666    0.03239    0.01703         45        640: 1\n",
      "tensor([1.40574], device='cuda:0', grad_fn=<AddBackward0>) tensor(8566.23438, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.71      0.646      0.702      0.435\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.08G    0.03656    0.03235    0.01663         20        640: 1\n",
      "tensor([1.24300], device='cuda:0', grad_fn=<AddBackward0>) tensor(8488.58691, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.702      0.671      0.713      0.444\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.08G    0.03627    0.03224    0.01618         38        640: 1\n",
      "tensor([1.50654], device='cuda:0', grad_fn=<AddBackward0>) tensor(8552.82422, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.701      0.669      0.714      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.08G    0.03599    0.03176    0.01579         33        640: 1\n",
      "tensor([1.49198], device='cuda:0', grad_fn=<AddBackward0>) tensor(8177.49072, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.694      0.668      0.715      0.447\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.08G    0.03579     0.0318    0.01572         28        640: 1\n",
      "tensor([1.38086], device='cuda:0', grad_fn=<AddBackward0>) tensor(8722.09766, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.722      0.669      0.724      0.458\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.08G    0.03554    0.03175    0.01513         27        640: 1\n",
      "tensor([1.42888], device='cuda:0', grad_fn=<AddBackward0>) tensor(9491.71777, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.71      0.682      0.729      0.461\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.08G    0.03523    0.03118    0.01508         29        640: 1\n",
      "tensor([1.61635], device='cuda:0', grad_fn=<AddBackward0>) tensor(8985.21875, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.718      0.672      0.731      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.08G    0.03512    0.03132    0.01482         35        640: 1\n",
      "tensor([1.28328], device='cuda:0', grad_fn=<AddBackward0>) tensor(8346.31445, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.736      0.671       0.74      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.08G    0.03475    0.03103     0.0148         31        640: 1\n",
      "tensor([1.23706], device='cuda:0', grad_fn=<AddBackward0>) tensor(8110.67334, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.719      0.687      0.737       0.47\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.08G    0.03475    0.03091    0.01464         40        640: 1\n",
      "tensor([1.48350], device='cuda:0', grad_fn=<AddBackward0>) tensor(7494.50000, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.738       0.68      0.739      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.08G    0.03444    0.03122    0.01415         29        640: 1\n",
      "tensor([1.25591], device='cuda:0', grad_fn=<AddBackward0>) tensor(7482.79785, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.727      0.689      0.742      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.08G    0.03424    0.03078    0.01384         26        640: 1\n",
      "tensor([1.14809], device='cuda:0', grad_fn=<AddBackward0>) tensor(7243.53369, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.725      0.691      0.741      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.08G    0.03401    0.03095    0.01375         45        640: 1\n",
      "tensor([1.42283], device='cuda:0', grad_fn=<AddBackward0>) tensor(7852.09863, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.738      0.681      0.745      0.481\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.08G    0.03368    0.03043     0.0135         36        640: 1\n",
      "tensor([1.34608], device='cuda:0', grad_fn=<AddBackward0>) tensor(8446.79004, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.682      0.746      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.08G    0.03335    0.02998     0.0131         20        640: 1\n",
      "tensor([1.36499], device='cuda:0', grad_fn=<AddBackward0>) tensor(7975.94287, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.734      0.692      0.747      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.08G    0.03304    0.03003    0.01336         25        640: 1\n",
      "tensor([1.16460], device='cuda:0', grad_fn=<AddBackward0>) tensor(7034.98535, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.743       0.69      0.747      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.08G    0.03288    0.03007    0.01278         34        640: 1\n",
      "tensor([1.29616], device='cuda:0', grad_fn=<AddBackward0>) tensor(7831.36084, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.736      0.698      0.752      0.489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.08G    0.03288    0.03002    0.01263         47        640: 1\n",
      "tensor([1.33060], device='cuda:0', grad_fn=<AddBackward0>) tensor(7621.95654, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.729      0.702      0.754      0.492\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.08G    0.03242    0.02959    0.01256         35        640: 1\n",
      "tensor([1.20683], device='cuda:0', grad_fn=<AddBackward0>) tensor(7033.05908, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.726       0.71      0.753      0.493\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.08G    0.03226     0.0299    0.01244         41        640: 1\n",
      "tensor([1.33105], device='cuda:0', grad_fn=<AddBackward0>) tensor(7718.14551, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.727      0.712      0.755      0.495\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.08G     0.0322     0.0296    0.01209         41        640: 1\n",
      "tensor([1.49904], device='cuda:0', grad_fn=<AddBackward0>) tensor(8363.53125, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.741      0.705      0.756      0.496\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.08G    0.03194    0.02955    0.01215         23        640: 1\n",
      "tensor([1.37431], device='cuda:0', grad_fn=<AddBackward0>) tensor(7870.86230, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.742      0.705      0.756      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.08G    0.03153    0.02916    0.01177         29        640: 1\n",
      "tensor([1.34529], device='cuda:0', grad_fn=<AddBackward0>) tensor(7684.86475, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.742      0.705      0.755      0.496\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.08G    0.03131    0.02921    0.01151         33        640: 1\n",
      "tensor([1.23331], device='cuda:0', grad_fn=<AddBackward0>) tensor(7452.95410, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.744      0.707      0.756      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.08G    0.03105    0.02892    0.01142         33        640: 1\n",
      "tensor([1.24513], device='cuda:0', grad_fn=<AddBackward0>) tensor(7611.78467, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.708      0.756      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.08G      0.031    0.02872    0.01137         34        640: 1\n",
      "tensor([1.24796], device='cuda:0', grad_fn=<AddBackward0>) tensor(7113.68115, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.734       0.71      0.756      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.08G    0.03076    0.02865    0.01119         35        640: 1\n",
      "tensor([1.25373], device='cuda:0', grad_fn=<AddBackward0>) tensor(7422.79492, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.736      0.709      0.756      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.08G    0.03057    0.02873    0.01108         33        640: 1\n",
      "tensor([1.18314], device='cuda:0', grad_fn=<AddBackward0>) tensor(7667.88965, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.733      0.711      0.756      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.08G    0.03022    0.02829    0.01071         34        640: 1\n",
      "tensor([1.44884], device='cuda:0', grad_fn=<AddBackward0>) tensor(8086.39795, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.736       0.71      0.756      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.08G    0.02988    0.02811     0.0105         40        640: 1\n",
      "tensor([1.35971], device='cuda:0', grad_fn=<AddBackward0>) tensor(6976.82422, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.734      0.713      0.757      0.499\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.08G    0.02986    0.02814    0.01041         42        640: 1\n",
      "tensor([1.17024], device='cuda:0', grad_fn=<AddBackward0>) tensor(7469.04883, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.709      0.757        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.08G    0.02956    0.02808    0.01042         26        640: 1\n",
      "tensor([1.04106], device='cuda:0', grad_fn=<AddBackward0>) tensor(7358.64062, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.741      0.708      0.756      0.499\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.08G    0.02925    0.02812    0.01025         21        640: 1\n",
      "tensor([1.07549], device='cuda:0', grad_fn=<AddBackward0>) tensor(7245.13574, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.728      0.716      0.755      0.499\n",
      "\n",
      "50 epochs completed in 2.140 hours.\n",
      "Optimizer stripped from runs/train/increment_VOC_Lwf/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/increment_VOC_Lwf/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/increment_VOC_Lwf/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.738       0.71      0.757        0.5\n",
      "                   car       4952       1201      0.794      0.864      0.897      0.672\n",
      "                person       4952       4528      0.823       0.79      0.857      0.543\n",
      "             aeroplane       4952        285      0.865      0.775      0.844      0.525\n",
      "               bicycle       4952        337      0.833       0.74      0.853      0.571\n",
      "                  bird       4952        459      0.716      0.632      0.675       0.41\n",
      "                  boat       4952        263      0.567       0.62      0.626      0.363\n",
      "                bottle       4952        469      0.669      0.682      0.684      0.448\n",
      "                   bus       4952        213      0.799      0.784       0.84      0.674\n",
      "                   cat       4952        358      0.789      0.698      0.764      0.508\n",
      "                 chair       4952        756      0.613      0.573      0.606       0.38\n",
      "                   cow       4952        244      0.727      0.806      0.804      0.569\n",
      "           diningtable       4952        206      0.705      0.684      0.701      0.438\n",
      "                   dog       4952        489      0.754       0.64       0.74      0.464\n",
      "                 horse       4952        348      0.821       0.83      0.871      0.581\n",
      "             motorbike       4952        325      0.782       0.74      0.826      0.515\n",
      "           pottedplant       4952        480      0.582      0.481      0.501      0.248\n",
      "                 sheep       4952        242      0.681      0.785      0.795      0.556\n",
      "                  sofa       4952        239      0.672      0.611      0.693      0.482\n",
      "                 train       4952        282      0.836      0.766      0.821      0.537\n",
      "             tvmonitor       4952        308      0.729      0.695      0.735      0.508\n",
      "Results saved to \u001b[1mruns/train/increment_VOC_Lwf\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : increment_VOC_Lwf\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/0a838d5ecabe4d46829230bb1fd3c410\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8175870900078422\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 35.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8443482692666566\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.525247067890371\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.8645808568985652\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.775438596491228\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 221.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.784023952453857\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 50.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.8528565162384543\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.5706794603769377\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8330690098000887\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.7404326515767333\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 250.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.671193293918542\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 115.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.6754290790619458\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.4095409103063122\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7160023327018499\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.6316624358563357\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 290.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.5922911074660723\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 124.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6259783074784999\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.3631074871109895\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.5671438825232863\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6197718631178707\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 163.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6754640625618273\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 158.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.6838424805848073\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.44839534507648066\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6687610814059745\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.6823027718550106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 320.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.7912135468119574\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8400039246147666\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.6742961346573844\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.7985221063193773\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.784037558685446\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 167.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8274136305807966\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 270.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.8972014591054798\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.672454142615013\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.7935639026302815\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8642797668609492\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1038.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.740952282840634\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 67.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.7643744975362693\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.5080695680604411\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.7891232620727301\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.6983240223463687\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 250.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.5924116796833039\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 273.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6064943141518351\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.38045323412080884\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6134697438952088\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.5727513227513228\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 433.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.7641465260524876\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 74.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8036389038975261\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.5694607436383448\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7265573244526368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8058373632144123\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 197.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6944824352344916\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 59.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7011295874811598\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.43832678115738605\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7047963635277067\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6844660194174758\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 141.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.692359541732868\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 102.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.73999650011156\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.46425217441519895\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.7539361728116748\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.6400817995910021\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 313.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8255828109220162\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 63.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.8705662095230036\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.5810511914489094\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8207627981798684\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8304597701149425\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 289.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (1.399193286895752, 7.278562545776367)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.08970917579568985, 0.7569098576554378)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.03976387380408377, 0.49971718207726656)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.3103438224915192, 0.7437226896358331)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.10377558923246917, 0.7157927706423677)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.7604677050632058\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 67.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8259266134055144\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5145448710166082\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.7821109560079059\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.7399900599900601\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 240.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.805979635115421\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 771.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8566632862314448\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5434001015585928\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8226478061625937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.7899734982332155\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3577.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.526473227112945\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 166.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5008416309953913\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.24768772332994674\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.5816613085364835\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.48085012095428764\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 231.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7292389061488168\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 89.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.7950375272972939\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.5562794891970412\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.68078097138357\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.7851239669421488\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 190.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6398583290446235\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 71.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.6932121751429505\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.4824857811414772\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6717244813046613\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6108786610878661\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 146.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.029245130717754364, 0.06964188069105148)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.010251629166305065, 0.06543632596731186)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.028084082528948784, 0.04515719413757324)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.7994999974371307\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8206163684606326\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.5369109118212323\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8361148577867623\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.7659574468085106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 216.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7114331224819485\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 80.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.734703920555659\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5084557783544807\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7288764357288912\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.6948051948051948\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 214.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.03179612010717392, 0.051952846348285675)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.009738685563206673, 0.0493868924677372)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.018925191834568977, 0.0250551737844944)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : increment_VOC_Lwf\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/0a838d5ecabe4d46829230bb1fd3c410\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/increment_VOC_Lwf\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.18 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_Lwf.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-4 \\\n",
    "--name increment_VOC_Lwf \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e673cdd6-d09b-4335-9d7e-32248704d165",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_Lwf/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.713      0.227      0.439      0.217\n",
      "                   car       2244       8711      0.613      0.823      0.797      0.461\n",
      "                   van       2244        861      0.851       0.18        0.5      0.274\n",
      "                 truck       2244        333          1     0.0577      0.588       0.34\n",
      "                  tram       2244        138          1     0.0132      0.508      0.227\n",
      "                person       2244       1286      0.384      0.586      0.494      0.239\n",
      "        person_sitting       2244         89      0.271      0.101      0.186     0.0447\n",
      "               cyclist       2244        496      0.583     0.0452      0.242     0.0591\n",
      "                  misc       2244        284          1    0.00651      0.195     0.0873\n",
      "Speed: 0.1ms pre-process, 0.8ms inference, 1.0ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp160\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/increment_VOC_Lwf/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eddc867a-e9f1-4fab-97ad-0679d1c729b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b674b5b-7cd4-4263-945f-f3b07100157a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01f841e1-5846-450b-b926-338c293533af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4a486d6b-e70f-4837-bde6-605e742dbec9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_Lwf: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_Lwf_1e-3, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=0.001, Lwf_temperature=1.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/2a4656d8c7434ca791f7063c961e4edd\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83607  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 214 layers, 7089751 parameters, 7089751 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from runs/train/fog_02/weights/best.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 16551 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_Lwf_1e-3/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_Lwf_1e-3\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       3.5G    0.08847    0.04649    0.07579         71        640:  \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "       0/49       3.5G    0.08141    0.04743    0.07423         36        640: 1\n",
      "tensor([3.22612], device='cuda:0', grad_fn=<AddBackward0>) tensor(1991.68469, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769     0.0247     0.0157     0.0051\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.08G     0.0667    0.04545    0.06904         58        640: 1\n",
      "tensor([4.06569], device='cuda:0', grad_fn=<AddBackward0>) tensor(2645.21436, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.591     0.0547     0.0447     0.0171\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.08G    0.05973    0.04388    0.06329         37        640: 1\n",
      "tensor([5.63016], device='cuda:0', grad_fn=<AddBackward0>) tensor(4559.29297, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.533      0.108     0.0843     0.0349\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.08G     0.0553    0.04221      0.059         46        640: 1\n",
      "tensor([6.09883], device='cuda:0', grad_fn=<AddBackward0>) tensor(5022.70117, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.372      0.146       0.11     0.0484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.08G     0.0535    0.04159     0.0559         39        640: 1\n",
      "tensor([5.52551], device='cuda:0', grad_fn=<AddBackward0>) tensor(4443.91455, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.313      0.218       0.16     0.0715\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.08G    0.05197     0.0413    0.05289         28        640: 1\n",
      "tensor([5.61837], device='cuda:0', grad_fn=<AddBackward0>) tensor(4663.42773, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.28      0.238       0.18     0.0802\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.08G    0.05124    0.04128    0.05047         39        640: 1\n",
      "tensor([5.57349], device='cuda:0', grad_fn=<AddBackward0>) tensor(4621.60791, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.289      0.264      0.208     0.0934\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.08G    0.05076    0.04084    0.04943         34        640: 1\n",
      "tensor([5.76454], device='cuda:0', grad_fn=<AddBackward0>) tensor(4829.00146, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.303      0.298      0.235      0.104\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.08G    0.05023    0.04064    0.04773         41        640: 1\n",
      "tensor([4.62890], device='cuda:0', grad_fn=<AddBackward0>) tensor(3794.78174, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.323      0.312      0.254      0.115\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.08G    0.04966     0.0399    0.04627         46        640: 1\n",
      "tensor([4.74514], device='cuda:0', grad_fn=<AddBackward0>) tensor(3789.23828, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.335      0.324      0.269      0.121\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.08G    0.04972    0.04031    0.04516         30        640: 1\n",
      "tensor([4.60763], device='cuda:0', grad_fn=<AddBackward0>) tensor(3682.29248, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.362      0.332      0.286       0.13\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.08G    0.04932     0.0407    0.04444         26        640: 1\n",
      "tensor([4.66265], device='cuda:0', grad_fn=<AddBackward0>) tensor(3861.03247, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.36      0.346      0.295      0.136\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.08G    0.04896    0.04053    0.04367         33        640: 1\n",
      "tensor([4.56569], device='cuda:0', grad_fn=<AddBackward0>) tensor(3726.27905, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.367      0.355      0.303      0.142\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.08G    0.04899    0.04041    0.04306         30        640: 1\n",
      "tensor([4.30517], device='cuda:0', grad_fn=<AddBackward0>) tensor(3380.86401, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.391      0.357       0.32      0.148\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.08G    0.04892    0.04024    0.04255         33        640: 1\n",
      "tensor([4.47910], device='cuda:0', grad_fn=<AddBackward0>) tensor(3572.35645, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.386      0.362      0.321       0.15\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.08G    0.04864    0.04028     0.0423         23        640: 1\n",
      "tensor([3.70890], device='cuda:0', grad_fn=<AddBackward0>) tensor(2809.05444, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.382      0.377      0.325      0.155\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.08G    0.04827    0.04001    0.04188         40        640: 1\n",
      "tensor([4.28703], device='cuda:0', grad_fn=<AddBackward0>) tensor(3348.68188, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.381      0.376      0.329      0.156\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.08G    0.04806    0.04023    0.04119         45        640: 1\n",
      "tensor([3.44802], device='cuda:0', grad_fn=<AddBackward0>) tensor(2578.03174, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.397      0.384      0.343      0.163\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.08G    0.04819    0.04028     0.0411         20        640: 1\n",
      "tensor([3.67825], device='cuda:0', grad_fn=<AddBackward0>) tensor(3055.33447, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.396      0.386      0.349      0.168\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.08G    0.04801    0.04031    0.04095         38        640: 1\n",
      "tensor([3.37506], device='cuda:0', grad_fn=<AddBackward0>) tensor(2443.04419, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.41      0.396      0.354      0.169\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.08G    0.04791    0.03984    0.04055         33        640: 1\n",
      "tensor([3.45633], device='cuda:0', grad_fn=<AddBackward0>) tensor(2581.30420, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.415      0.395      0.359      0.171\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.08G    0.04783    0.04018    0.04042         28        640: 1\n",
      "tensor([3.15308], device='cuda:0', grad_fn=<AddBackward0>) tensor(2436.28052, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.42      0.393      0.364      0.174\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.08G    0.04761    0.04029    0.04014         27        640: 1\n",
      "tensor([3.55355], device='cuda:0', grad_fn=<AddBackward0>) tensor(2740.15918, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.412      0.405      0.368      0.176\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.08G    0.04749    0.03959     0.0399         29        640: 1\n",
      "tensor([3.83696], device='cuda:0', grad_fn=<AddBackward0>) tensor(2886.99927, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.426      0.402      0.373       0.18\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.08G    0.04749    0.04003    0.03988         35        640: 1\n",
      "tensor([2.94733], device='cuda:0', grad_fn=<AddBackward0>) tensor(2150.85669, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.428      0.407      0.377      0.183\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.08G    0.04747    0.03977    0.03988         31        640: 1\n",
      "tensor([3.23307], device='cuda:0', grad_fn=<AddBackward0>) tensor(2482.53589, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.41      0.416      0.379      0.182\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.08G    0.04752    0.03972    0.03972         40        640: 1\n",
      "tensor([2.92947], device='cuda:0', grad_fn=<AddBackward0>) tensor(1983.54175, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.425      0.411      0.376      0.182\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.08G     0.0475     0.0403    0.03952         29        640: 1\n",
      "tensor([2.51878], device='cuda:0', grad_fn=<AddBackward0>) tensor(1741.40979, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.422      0.413       0.38      0.183\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.08G    0.04739    0.04001    0.03953         26        640: 1\n",
      "tensor([2.49549], device='cuda:0', grad_fn=<AddBackward0>) tensor(1768.87817, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.439      0.406      0.383      0.186\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.08G    0.04733    0.04037    0.03945         45        640: 1\n",
      "tensor([2.71556], device='cuda:0', grad_fn=<AddBackward0>) tensor(1826.69653, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.427      0.415      0.386      0.188\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.08G    0.04716    0.03987    0.03913         36        640: 1\n",
      "tensor([2.63536], device='cuda:0', grad_fn=<AddBackward0>) tensor(1822.87927, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.442      0.417      0.389       0.19\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.08G    0.04696    0.03948    0.03899         20        640: 1\n",
      "tensor([2.62936], device='cuda:0', grad_fn=<AddBackward0>) tensor(1714.62488, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.442      0.411      0.391       0.19\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.08G    0.04699    0.03966    0.03936         25        640: 1\n",
      "tensor([2.20257], device='cuda:0', grad_fn=<AddBackward0>) tensor(1455.75977, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.434      0.419      0.389      0.189\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.08G    0.04699    0.03995    0.03912         34        640: 1\n",
      "tensor([2.49729], device='cuda:0', grad_fn=<AddBackward0>) tensor(1522.68408, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.437      0.422      0.392      0.191\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.08G     0.0471    0.04002    0.03894         47        640: 1\n",
      "tensor([2.14669], device='cuda:0', grad_fn=<AddBackward0>) tensor(1285.42651, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.445      0.417      0.393      0.192\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.08G     0.0466    0.03956    0.03877         35        640: 1\n",
      "tensor([2.28340], device='cuda:0', grad_fn=<AddBackward0>) tensor(1352.68579, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.436      0.426      0.395      0.192\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.08G    0.04671    0.04024    0.03874         41        640: 1\n",
      "tensor([2.24127], device='cuda:0', grad_fn=<AddBackward0>) tensor(1314.56641, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.443      0.427      0.395      0.193\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.08G    0.04694    0.03989    0.03887         41        640: 1\n",
      "tensor([2.43720], device='cuda:0', grad_fn=<AddBackward0>) tensor(1356.98120, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.432       0.43      0.398      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.08G    0.04687     0.0401    0.03931         23        640: 1\n",
      "tensor([2.13593], device='cuda:0', grad_fn=<AddBackward0>) tensor(1220.69763, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.431      0.431      0.398      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.08G    0.04648    0.03975    0.03875         29        640: 1\n",
      "tensor([1.98270], device='cuda:0', grad_fn=<AddBackward0>) tensor(1107.75293, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.44      0.427      0.397      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.08G    0.04666    0.04003    0.03871         33        640: 1\n",
      "tensor([1.81783], device='cuda:0', grad_fn=<AddBackward0>) tensor(896.34143, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.441       0.43      0.398      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.08G    0.04651    0.03983    0.03834         33        640: 1\n",
      "tensor([1.84446], device='cuda:0', grad_fn=<AddBackward0>) tensor(940.13831, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.443      0.429      0.399      0.196\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.08G    0.04663    0.03964    0.03881         34        640: 1\n",
      "tensor([1.64436], device='cuda:0', grad_fn=<AddBackward0>) tensor(769.34973, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.442      0.431      0.398      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.08G    0.04647    0.03967    0.03851         35        640: 1\n",
      "tensor([1.72626], device='cuda:0', grad_fn=<AddBackward0>) tensor(843.65912, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.441       0.43      0.398      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.08G    0.04664    0.03988    0.03864         33        640: 1\n",
      "tensor([1.59799], device='cuda:0', grad_fn=<AddBackward0>) tensor(757.46405, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.448      0.425      0.399      0.195\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.08G    0.04638    0.03955    0.03886         34        640: 1\n",
      "tensor([1.81223], device='cuda:0', grad_fn=<AddBackward0>) tensor(799.47742, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.445      0.429      0.401      0.196\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.08G    0.04624    0.03964    0.03814         40        640: 1\n",
      "tensor([1.66541], device='cuda:0', grad_fn=<AddBackward0>) tensor(598.14215, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.445      0.429        0.4      0.197\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.08G    0.04628    0.03986    0.03854         42        640: 1\n",
      "tensor([1.52803], device='cuda:0', grad_fn=<AddBackward0>) tensor(645.69019, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.444       0.43      0.401      0.197\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.08G    0.04606    0.03981    0.03851         26        640: 1\n",
      "tensor([1.29913], device='cuda:0', grad_fn=<AddBackward0>) tensor(739.05695, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.448      0.428      0.401      0.197\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.08G    0.04598    0.04003    0.03788         21        640: 1\n",
      "tensor([1.24285], device='cuda:0', grad_fn=<AddBackward0>) tensor(522.46332, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.451      0.426      0.402      0.196\n",
      "\n",
      "50 epochs completed in 1.875 hours.\n",
      "Optimizer stripped from runs/train/increment_VOC_Lwf_1e-3/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/increment_VOC_Lwf_1e-3/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/increment_VOC_Lwf_1e-3/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.449      0.427      0.401      0.197\n",
      "                   car       4952       1201      0.631       0.69      0.704      0.449\n",
      "                person       4952       4528      0.606      0.562      0.599      0.285\n",
      "             aeroplane       4952        285      0.424       0.47      0.431      0.191\n",
      "               bicycle       4952        337      0.555      0.481      0.519      0.257\n",
      "                  bird       4952        459      0.363      0.231      0.232     0.0991\n",
      "                  boat       4952        263      0.291      0.376      0.231     0.0922\n",
      "                bottle       4952        469      0.374      0.407      0.334       0.15\n",
      "                   bus       4952        213      0.514      0.606      0.552      0.356\n",
      "                   cat       4952        358      0.535      0.296      0.367      0.145\n",
      "                 chair       4952        756      0.374      0.287      0.271      0.129\n",
      "                   cow       4952        244       0.44      0.503      0.432      0.252\n",
      "           diningtable       4952        206      0.306      0.237      0.213     0.0715\n",
      "                   dog       4952        489      0.475      0.231      0.318      0.131\n",
      "                 horse       4952        348      0.472      0.543      0.463      0.193\n",
      "             motorbike       4952        325      0.566      0.569      0.561      0.261\n",
      "           pottedplant       4952        480      0.332      0.252      0.219     0.0766\n",
      "                 sheep       4952        242      0.356      0.562      0.459      0.267\n",
      "                  sofa       4952        239       0.45      0.298      0.284      0.144\n",
      "                 train       4952        282      0.474       0.48       0.43      0.175\n",
      "             tvmonitor       4952        308      0.442      0.448        0.4      0.214\n",
      "Results saved to \u001b[1mruns/train/increment_VOC_Lwf_1e-3\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : increment_VOC_Lwf_1e-3\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/2a4656d8c7434ca791f7063c961e4edd\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.4458649648335235\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 182.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.43139234115016656\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.1908418397449467\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.42394484889393647\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.47017543859649125\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 134.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.515146570305303\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 130.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.5188734716595516\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.257023241244364\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.5548948318110513\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.4807121661721068\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 162.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.2822754667764313\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 186.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.23213082113091696\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.09906571588476926\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.3629647109356306\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.23093681917211328\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 106.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.328559297038985\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 241.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.23115355200453147\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.09219289144083019\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.2914928723622113\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.376425855513308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 99.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.3900102127741944\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 319.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.3342266700690167\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.1497129920086012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.3741711931373793\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.4072494669509595\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 191.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.5557781315743375\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 122.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.551692643117025\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.3561834293066991\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.5135063745009925\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.6056338028169014\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 129.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.6594998522942951\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 484.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.703610663921822\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.44925472334726\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.6313658566045905\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.6902581182348043\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 829.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.381258985547791\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 92.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.3674452437404952\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.1448518904673484\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.5352117086716007\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.29608938547486036\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 106.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.3249624039384942\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 363.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.2714189943768999\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.12901969786333287\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.37443547114501796\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.28703703703703703\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 217.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.46972296385379314\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 156.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.4318769358412066\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.2524184460906683\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.4404205396701722\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.5032024458253966\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 123.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.26709399256564087\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 111.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.21345258403050416\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.07150689224377298\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.3056378115536921\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.237182975856115\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 49.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.3109400050163471\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 125.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.31842349144112775\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.13084663035302307\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.4748594274806433\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.23114853094403195\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 113.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.5053262758136998\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 211.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.4633044495065392\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.19326501263903978\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.47246272831434927\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.5431034482758621\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 189.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (1.9040480852127075, 38.06584930419922)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.015664729747640438, 0.4017650115375864)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.0050968065539014045, 0.19702934079893197)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.27989808076318207, 0.7694054044839019)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.024676350023390404, 0.4310739818419035)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.5674389131578064\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 142.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.561144101828511\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.26053397960451935\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.5656583026924391\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.5692307692307692\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 185.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.5830076556738399\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 1655.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.5989406045805505\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.28472215593540084\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.6058357231112649\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.5618374558303887\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 2544.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.2867119952059618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 243.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.21940057908077518\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.07663088613065765\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.33236956912756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.2520833333333333\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 121.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.4359780515879308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 246.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.4590293582732082\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.2671768200386383\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.3561285103657752\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.5619834710743802\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 136.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.3587964824069891\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 87.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.2843139912961924\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.144047105795823\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.45030132831661374\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.29819987351368105\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 71.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.045984897762537, 0.08140544593334198)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.03788098320364952, 0.07423443347215652)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.039479490369558334, 0.047431107610464096)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.47681215007604744\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 150.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.4296283008995932\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.17451767891220932\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.4741104741104741\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.47954479301997033\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 135.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.4449322488428389\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 174.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.4004703104309968\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.213829046770084\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.4418556929616158\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.44805194805194803\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 138.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.0443616546690464, 0.0682673379778862)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.02948067896068096, 0.06387028098106384)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.024048147723078728, 0.02854139916598797)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : increment_VOC_Lwf_1e-3\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/2a4656d8c7434ca791f7063c961e4edd\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/increment_VOC_Lwf_1e-3\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.23 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 81.00 KB/505.00 KB\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_Lwf.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-3 \\\n",
    "--name increment_VOC_Lwf_1e-3 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6897ad49-27df-4658-982d-09b53cacae6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_Lwf_1e-3/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 157 layers, 7080247 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.696      0.612      0.691      0.364\n",
      "                   car       2244       8711      0.705      0.872       0.87      0.541\n",
      "                   van       2244        861      0.772      0.713      0.793      0.465\n",
      "                 truck       2244        333       0.94      0.739      0.875      0.525\n",
      "                  tram       2244        138      0.966      0.627      0.861      0.486\n",
      "                person       2244       1286        0.4      0.618      0.555      0.264\n",
      "        person_sitting       2244         89       0.26      0.573      0.435      0.163\n",
      "               cyclist       2244        496      0.662      0.448       0.51      0.157\n",
      "                  misc       2244        284      0.863       0.31      0.626      0.313\n",
      "Speed: 0.1ms pre-process, 0.9ms inference, 1.0ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp161\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/increment_VOC_Lwf_1e-3/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "502019ff-39a9-4bca-88c6-b329c353be53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30652a6a-2c84-4eba-93d2-41524754fc57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "387649b1-97bd-46e4-b7a9-102c6f5745d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f303798-70da-4730-9d97-c0dea17a5b9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "b5784b70-ad5c-47d8-a0dd-886e8a49f9f8",
   "metadata": {},
   "source": [
    "接下来是回放。我的实验计划是这样的，用全部的数据（包含部分选取的kitti）训练一个baseline、一个Lwf模型。\n",
    "然后将全部数据划分为train和val，其中train用来训练一个新的baseline2和Lwf2，再分别应用Val训练BiC的内容，接着比较这四个模型的性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5d9117a-ec46-45eb-a66c-b51a435fe74a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC_base.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_Replay_base, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=False\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/917dcc93dd6544e1a8cd04b53bf4831a\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old... 18599 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.17 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_Replay_base/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_Replay_base\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.64G     0.1096    0.04518    0.08268         88        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/49      3.64G     0.0652    0.04463    0.05866         64        640: 1\n",
      "tensor([1.16714], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.38      0.171      0.134     0.0651\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.22G    0.04746    0.03568    0.03728         21        640: 1\n",
      "tensor([0.66947], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.43      0.484      0.419      0.225\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.22G    0.04625    0.03543    0.02716         31        640: 1\n",
      "tensor([0.69680], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.531      0.502      0.491       0.25\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.22G    0.04537    0.03591    0.02436         29        640: 1\n",
      "tensor([0.54324], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.554       0.53      0.534      0.281\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.22G    0.04387     0.0358     0.0222         44        640: 1\n",
      "tensor([0.62132], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.643      0.588      0.628      0.352\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.22G     0.0425    0.03524    0.02017         30        640: 1\n",
      "tensor([0.52661], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.668      0.618      0.661       0.38\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.22G    0.04151    0.03448    0.01889         44        640: 1\n",
      "tensor([0.64950], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.655      0.621      0.659      0.386\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.22G    0.04062    0.03413     0.0177         41        640: 1\n",
      "tensor([0.59894], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.679      0.632      0.679      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.22G    0.04002    0.03376    0.01707         35        640: 1\n",
      "tensor([0.51854], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.693      0.655      0.702      0.426\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.22G    0.03949    0.03345    0.01621         33        640: 1\n",
      "tensor([0.62019], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.697       0.65      0.701      0.433\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.22G    0.03877    0.03346    0.01528         59        640: 1\n",
      "tensor([0.60276], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.704      0.664      0.719      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.22G    0.03848    0.03311     0.0149         34        640: 1\n",
      "tensor([0.53350], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.722      0.679      0.734      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.22G    0.03814    0.03269    0.01461         56        640: 1\n",
      "tensor([0.73221], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.72      0.683      0.743      0.469\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.22G    0.03764    0.03242    0.01387         36        640: 1\n",
      "tensor([0.55025], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.728      0.691      0.747      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.22G    0.03716    0.03212    0.01332         69        640: 1\n",
      "tensor([0.77369], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.722      0.717      0.758      0.489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.22G    0.03687    0.03205    0.01298         51        640: 1\n",
      "tensor([0.60417], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.741      0.712      0.766      0.499\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.22G    0.03652    0.03193     0.0125         43        640: 1\n",
      "tensor([0.58963], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.743      0.709      0.768      0.499\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.22G    0.03639    0.03166    0.01232         27        640: 1\n",
      "tensor([0.56024], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.716      0.772      0.505\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.22G    0.03595    0.03142    0.01226         33        640: 1\n",
      "tensor([0.58913], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.747      0.709      0.772      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.22G    0.03601     0.0316    0.01203         46        640: 1\n",
      "tensor([0.61243], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.754      0.724      0.781      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.22G    0.03555    0.03093    0.01151         46        640: 1\n",
      "tensor([0.62468], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.747      0.727      0.781      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.22G    0.03528    0.03065    0.01131         29        640: 1\n",
      "tensor([0.59876], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.754       0.73      0.783      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.22G    0.03514    0.03061    0.01111         35        640: 1\n",
      "tensor([0.54151], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.747      0.731      0.784      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.22G    0.03482    0.03073    0.01077         37        640: 1\n",
      "tensor([0.49323], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.729      0.789      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.22G    0.03439    0.03056    0.01055         31        640: 1\n",
      "tensor([0.46916], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.725       0.79      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.22G    0.03424    0.03003    0.01031         47        640: 1\n",
      "tensor([0.64347], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.781      0.729        0.8      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.22G     0.0341    0.03017    0.01006         40        640: 1\n",
      "tensor([0.47369], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.758      0.749      0.803      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.22G    0.03375    0.02983   0.009954         31        640: 1\n",
      "tensor([0.59784], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.746      0.803      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.22G    0.03358    0.02986   0.009779         26        640: 1\n",
      "tensor([0.33533], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.752       0.76      0.805      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.22G    0.03322    0.02939   0.009405         33        640: 1\n",
      "tensor([0.57711], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.755      0.806       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.22G    0.03303    0.02964   0.009471         40        640: 1\n",
      "tensor([0.55434], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.756      0.807      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.22G    0.03277    0.02937   0.009043         40        640: 1\n",
      "tensor([0.41402], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774      0.753      0.809      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.22G    0.03258    0.02939   0.008784         33        640: 1\n",
      "tensor([0.40409], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.784      0.751      0.812      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.22G    0.03228     0.0288   0.008687         36        640: 1\n",
      "tensor([0.53088], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.78      0.755      0.814      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.22G    0.03223    0.02883   0.008435         44        640: 1\n",
      "tensor([0.55272], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.783      0.762      0.813      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.22G    0.03177    0.02851   0.008394         34        640: 1\n",
      "tensor([0.36090], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.781      0.761      0.815      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.22G    0.03167    0.02843   0.008103         56        640: 1\n",
      "tensor([0.53077], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.765      0.817      0.567\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.22G    0.03135    0.02803   0.007867         44        640: 1\n",
      "tensor([0.44303], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.782       0.76      0.817       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.22G    0.03107    0.02771   0.007807         34        640: 1\n",
      "tensor([0.42042], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.794      0.758      0.819      0.571\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.22G    0.03093    0.02771   0.007603         48        640: 1\n",
      "tensor([0.53798], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.793      0.758       0.82      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.22G    0.03059    0.02773   0.007326         56        640: 1\n",
      "tensor([0.50948], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.802       0.75       0.82      0.573\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.22G    0.03031    0.02746   0.007229         36        640: 1\n",
      "tensor([0.39584], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.765      0.821      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.22G    0.03032    0.02733   0.007122         41        640: 1\n",
      "tensor([0.52131], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.788      0.767      0.821      0.575\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.22G    0.02984    0.02722   0.006919         38        640: 1\n",
      "tensor([0.35920], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.769      0.822      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.22G    0.02958    0.02667   0.006717         21        640: 1\n",
      "tensor([0.30585], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.769      0.822      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.22G    0.02936    0.02657   0.006607         40        640: 1\n",
      "tensor([0.42744], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.786      0.772      0.821      0.576\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.22G    0.02899    0.02628   0.006428         40        640: 1\n",
      "tensor([0.50106], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.768      0.821      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.22G    0.02904    0.02632    0.00619         31        640: 1\n",
      "tensor([0.32804], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.791      0.762       0.82      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.22G    0.02868    0.02605   0.006142         26        640: 1\n",
      "tensor([0.31889], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.794       0.76       0.82      0.577\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.22G    0.02842    0.02601   0.006073         30        640: 1\n",
      "tensor([0.37683], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.792      0.763      0.819      0.577\n",
      "\n",
      "50 epochs completed in 1.767 hours.\n",
      "Optimizer stripped from runs/train/increment_VOC_Replay_base/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/increment_VOC_Replay_base/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/increment_VOC_Replay_base/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.789      0.768      0.821      0.577\n",
      "                   car       4952       1201      0.819      0.888      0.921      0.709\n",
      "                person       4952       4528      0.858      0.806      0.889      0.592\n",
      "             aeroplane       4952        285      0.951      0.817      0.906      0.615\n",
      "               bicycle       4952        337      0.898      0.833       0.91      0.645\n",
      "                  bird       4952        459      0.772      0.739      0.781      0.509\n",
      "                  boat       4952        263      0.636      0.664      0.699      0.416\n",
      "                bottle       4952        469      0.665      0.725      0.747      0.507\n",
      "                   bus       4952        213      0.877      0.836      0.899      0.746\n",
      "                   cat       4952        358      0.848      0.827      0.858      0.645\n",
      "                 chair       4952        756      0.647      0.631      0.667      0.441\n",
      "                   cow       4952        244      0.774      0.828       0.87      0.654\n",
      "           diningtable       4952        206      0.746      0.689      0.758      0.528\n",
      "                   dog       4952        489      0.813      0.734      0.834      0.588\n",
      "                 horse       4952        348       0.87      0.876      0.907      0.652\n",
      "             motorbike       4952        325      0.854        0.8      0.888      0.579\n",
      "           pottedplant       4952        480      0.658      0.535      0.584      0.317\n",
      "                 sheep       4952        242      0.744      0.831       0.86      0.632\n",
      "                  sofa       4952        239      0.686      0.695      0.743       0.56\n",
      "                 train       4952        282       0.87      0.812      0.869      0.614\n",
      "             tvmonitor       4952        308      0.791      0.796      0.821      0.595\n",
      "Results saved to \u001b[1mruns/train/increment_VOC_Replay_base\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : increment_VOC_Replay_base\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/917dcc93dd6544e1a8cd04b53bf4831a\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.878754343469003\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.9057211731901395\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.615261300476263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9509738309738309\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.8167297121683087\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 233.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.863979380342157\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 32.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.9100642851987308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6446839228313024\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.897644354868352\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.8327482491676359\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 281.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.7548788838241471\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 100.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.7806996490460849\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.5091435479765535\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7719329262508949\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.738562091503268\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 339.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6495456726220291\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 100.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6985799312633793\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.41583007052333754\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6358289277997307\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6638672893425746\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 175.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6936574466039702\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 171.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7470598438196356\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.5073949495556029\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6649573822931821\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.7249466950959488\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 340.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8560210235711767\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 25.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8990893866538215\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7458617828572526\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8773761521961292\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8356807511737089\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 178.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8518777788676742\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 236.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9213180286870363\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.709131578589457\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8189250436056266\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.88759367194005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1066.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.8374420402117166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8584664334746549\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.6446643689247367\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8483451393062305\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.8268156424581006\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 296.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6387966693555127\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 260.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6668729060579235\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.4408995008613507\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.647121090177547\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.6306836954985103\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 477.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.8002543996913739\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 59.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8699362611094507\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6540808250529557\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7744227005952465\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8278688524590164\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 202.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.7167581093474372\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 48.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.758311175719245\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.5276099481350878\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7464706399776232\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6893203883495146\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 142.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7716950274979726\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 82.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.8338368232394358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.5876151278660602\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.813285564219271\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.7341513292433538\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 359.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8734525177661518\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 45.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.9073356132072429\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6516342070318253\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8704885077724583\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8764367816091954\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 305.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5815]                 : (0.4736921191215515, 3.876978635787964)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.13445833889100592, 0.8219185731930713)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.06506754146996986, 0.5769824167435804)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.3795762108497156, 0.801968655158871)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.1705004599536313, 0.7724406865245976)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.8261791999959153\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 44.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8883869719459561\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5793924233850224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.854129741657556\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 260.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8311042857642065\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 606.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.888781883530659\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5919016087952517\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8576248017292489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8061747701765369\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3650.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5905380982313093\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 133.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5839187712628227\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.3167752364783923\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6583116320683812\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.5354166666666667\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 257.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.784906532121155\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 69.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8597574844733608\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.631619013008981\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7439955871882106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8305785123966942\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 201.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6905545045358203\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 76.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.742575727398723\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.560497408762896\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6861052234540201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6950618680046853\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 166.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.02842377871274948, 0.06519940495491028)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.006072517484426498, 0.05865892022848129)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.026011599227786064, 0.04463255777955055)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.8398874435603326\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 34.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8692097202137543\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.6143169877753838\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8696934682887366\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.8120567375886525\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 229.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7934209648666186\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 65.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.8213089809157075\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.594735874976455\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7905093739285112\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7963540829394488\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 245.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.029965979978442192, 0.0459907241165638)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.005551920272409916, 0.044913239777088165)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.017528221011161804, 0.02265254780650139)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002579535683577)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : increment_VOC_Replay_base\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/917dcc93dd6544e1a8cd04b53bf4831a\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     BiC_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : 0.0001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/increment_VOC_Replay_base\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (2.99 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name increment_VOC_Replay_base \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7886267-5301-4e90-981a-59a0b1d73133",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_Replay_base/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.838      0.759      0.827      0.534\n",
      "                   car       2244       8711      0.906      0.874      0.943      0.704\n",
      "                   van       2244        861      0.874      0.815      0.886      0.632\n",
      "                 truck       2244        333      0.914      0.922      0.958      0.732\n",
      "                  tram       2244        138      0.839      0.855      0.924       0.58\n",
      "                person       2244       1286      0.871      0.619      0.749       0.39\n",
      "        person_sitting       2244         89      0.578      0.524      0.498       0.26\n",
      "               cyclist       2244        496      0.847      0.728       0.83      0.456\n",
      "                  misc       2244        284      0.874      0.733      0.824       0.52\n",
      "Speed: 0.1ms pre-process, 0.9ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp167\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/increment_VOC_Replay_base/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deb44c5d-3b64-4f6e-bc2d-a0b0f749973b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1ccee66-c67f-468f-afa3-7cfcde35059d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98ba3769-5ed1-4089-9c84-06110eafe3d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC_base.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_Replay_Lwf, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=False\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/2d83f4736ee84aa2b45673a521b136bf\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/best.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache... 18599 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.17 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_Replay_Lwf/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_Replay_Lwf\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       3.5G     0.1076    0.04545    0.07971         88        640:  error: RPC failed; curl 16 Error in the HTTP2 framing layer\n",
      "fatal: expected flush after ref listing\n",
      "       0/49       3.5G    0.06845    0.04668    0.05673         64        640: 1\n",
      "tensor([1.46333], device='cuda:0', grad_fn=<AddBackward0>) tensor(2556.11255, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.545      0.122      0.101     0.0439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.08G    0.05029      0.039    0.04147         21        640: 1\n",
      "tensor([1.22292], device='cuda:0', grad_fn=<AddBackward0>) tensor(4416.10400, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.31      0.268      0.212      0.101\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.08G    0.04754    0.03765    0.03404         31        640: 1\n",
      "tensor([1.50090], device='cuda:0', grad_fn=<AddBackward0>) tensor(7663.82129, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.397       0.38      0.349      0.168\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.08G    0.04529     0.0363    0.02762         29        640: 1\n",
      "tensor([1.31306], device='cuda:0', grad_fn=<AddBackward0>) tensor(7234.78027, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.51      0.504       0.49      0.252\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.08G    0.04361      0.036    0.02449         44        640: 1\n",
      "tensor([1.32094], device='cuda:0', grad_fn=<AddBackward0>) tensor(6817.16992, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.58       0.54      0.551      0.289\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.08G     0.0425    0.03558    0.02254         30        640: 1\n",
      "tensor([1.41228], device='cuda:0', grad_fn=<AddBackward0>) tensor(8235.33105, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.612      0.542      0.582      0.316\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.08G     0.0415    0.03477    0.02091         44        640: 1\n",
      "tensor([1.29916], device='cuda:0', grad_fn=<AddBackward0>) tensor(6899.13525, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.614      0.583      0.604      0.336\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.623      0.577       0.61      0.343\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.08G    0.04006    0.03415    0.01912         35        640: 1\n",
      "tensor([1.25772], device='cuda:0', grad_fn=<AddBackward0>) tensor(7201.16553, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.653      0.609      0.647      0.373\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.08G    0.03958    0.03393    0.01832         33        640: 1\n",
      "tensor([1.21365], device='cuda:0', grad_fn=<AddBackward0>) tensor(6517.19336, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.651      0.613      0.648      0.376\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.08G    0.03894    0.03405    0.01758         59        640: 1\n",
      "tensor([1.26557], device='cuda:0', grad_fn=<AddBackward0>) tensor(6652.66943, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.677      0.613       0.66      0.384\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.08G    0.03822    0.03335    0.01678         56        640: 1\n",
      "tensor([1.44559], device='cuda:0', grad_fn=<AddBackward0>) tensor(7336.94824, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.681       0.63      0.678      0.407\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.08G    0.03786    0.03311    0.01629         36        640: 1\n",
      "tensor([1.32610], device='cuda:0', grad_fn=<AddBackward0>) tensor(6938.42725, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.683      0.617      0.677      0.411\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.08G    0.03743    0.03286    0.01574         69        640: 1\n",
      "tensor([1.44876], device='cuda:0', grad_fn=<AddBackward0>) tensor(6444.18164, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.696       0.64      0.691      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.08G     0.0371    0.03278    0.01545         51        640: 1\n",
      "tensor([1.30239], device='cuda:0', grad_fn=<AddBackward0>) tensor(6569.87451, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.694      0.643      0.694      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.08G    0.03691    0.03274    0.01522         43        640: 1\n",
      "tensor([1.36503], device='cuda:0', grad_fn=<AddBackward0>) tensor(6946.44434, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.719      0.639      0.701      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.08G    0.03673    0.03247    0.01496         27        640: 1\n",
      "tensor([1.38029], device='cuda:0', grad_fn=<AddBackward0>) tensor(8066.52832, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.651      0.708       0.44\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.08G    0.03634    0.03235     0.0147         33        640: 1\n",
      "tensor([1.29833], device='cuda:0', grad_fn=<AddBackward0>) tensor(6810.70605, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.717      0.638        0.7      0.435\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.08G    0.03637    0.03247    0.01441         46        640: 1\n",
      "tensor([1.26556], device='cuda:0', grad_fn=<AddBackward0>) tensor(5963.69922, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.656      0.712      0.443\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.08G    0.03592    0.03187    0.01409         46        640: 1\n",
      "tensor([1.37823], device='cuda:0', grad_fn=<AddBackward0>) tensor(7684.19727, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.691      0.667      0.709      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.08G    0.03578     0.0316    0.01403         29        640: 1\n",
      "tensor([1.39988], device='cuda:0', grad_fn=<AddBackward0>) tensor(7501.35059, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.719      0.653      0.712      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.08G    0.03555    0.03162     0.0139         35        640: 1\n",
      "tensor([1.27884], device='cuda:0', grad_fn=<AddBackward0>) tensor(6985.87891, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.736      0.647      0.717      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.08G    0.03534    0.03181    0.01364         37        640: 1\n",
      "tensor([1.11754], device='cuda:0', grad_fn=<AddBackward0>) tensor(6230.98926, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.714       0.67      0.723      0.456\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.08G    0.03491    0.03164     0.0133         31        640: 1\n",
      "tensor([1.22616], device='cuda:0', grad_fn=<AddBackward0>) tensor(7289.08789, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.722      0.665      0.722      0.457\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.08G     0.0348    0.03112    0.01307         47        640: 1\n",
      "tensor([1.51939], device='cuda:0', grad_fn=<AddBackward0>) tensor(7810.56494, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.721      0.675      0.725       0.46\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.08G    0.03475    0.03135    0.01294         40        640: 1\n",
      "tensor([1.14541], device='cuda:0', grad_fn=<AddBackward0>) tensor(6883.82129, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.714      0.684       0.73      0.462\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.08G    0.03445    0.03103     0.0129         31        640: 1\n",
      "tensor([1.26690], device='cuda:0', grad_fn=<AddBackward0>) tensor(7445.35400, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.727      0.677      0.731      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.08G    0.03427    0.03111    0.01265         26        640: 1\n",
      "tensor([1.09770], device='cuda:0', grad_fn=<AddBackward0>) tensor(6771.24902, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.73      0.678      0.736       0.47\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.08G    0.03397    0.03067    0.01231         33        640: 1\n",
      "tensor([1.36604], device='cuda:0', grad_fn=<AddBackward0>) tensor(7106.98340, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.734      0.679      0.733      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.08G    0.03392    0.03106    0.01239         40        640: 1\n",
      "tensor([1.26852], device='cuda:0', grad_fn=<AddBackward0>) tensor(6719.21143, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.732      0.677      0.734      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.08G    0.03353    0.03075    0.01196         40        640: 1\n",
      "tensor([1.24190], device='cuda:0', grad_fn=<AddBackward0>) tensor(7555.70654, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.732      0.688      0.739      0.474\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.08G    0.03342    0.03091    0.01187         33        640: 1\n",
      "tensor([0.99253], device='cuda:0', grad_fn=<AddBackward0>) tensor(5342.80713, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.723      0.691      0.739      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.08G     0.0332    0.03033    0.01181         36        640: 1\n",
      "tensor([1.26202], device='cuda:0', grad_fn=<AddBackward0>) tensor(6633.73584, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.73      0.688      0.737      0.477\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.08G    0.03318    0.03041    0.01145         44        640: 1\n",
      "tensor([1.32781], device='cuda:0', grad_fn=<AddBackward0>) tensor(7376.30078, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   "
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-4 \\\n",
    "--name increment_VOC_Replay_Lwf \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1157f9be-8ebb-4a4a-95cd-4627c6ab0823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这个前端崩了只能通过结束时间计算大概是跑了两个小时"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4df7046e-915d-4ed6-a5da-7db4ee1800fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTI.yaml, weights=['runs/train/increment_VOC_Replay_Lwf/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.692      0.741      0.484\n",
      "                   car       4952       1201      0.794       0.87      0.894      0.663\n",
      "                person       4952       4528      0.817      0.782      0.849      0.532\n",
      "             aeroplane       4952        285      0.898      0.738      0.839      0.511\n",
      "               bicycle       4952        337       0.86      0.767      0.843      0.565\n",
      "                  bird       4952        459      0.687      0.618      0.651      0.383\n",
      "                  boat       4952        263      0.579      0.579      0.588      0.322\n",
      "                bottle       4952        469      0.658      0.635      0.654      0.417\n",
      "                   bus       4952        213      0.812      0.793      0.831      0.658\n",
      "                   cat       4952        358      0.806      0.683      0.767      0.497\n",
      "                 chair       4952        756      0.617      0.562      0.593      0.354\n",
      "                   cow       4952        244      0.721      0.779      0.801      0.566\n",
      "           diningtable       4952        206      0.707      0.609      0.688      0.429\n",
      "                   dog       4952        489      0.752      0.616       0.72      0.447\n",
      "                 horse       4952        348      0.814      0.806       0.86      0.577\n",
      "             motorbike       4952        325      0.811      0.748       0.82      0.516\n",
      "           pottedplant       4952        480      0.569      0.479      0.467      0.237\n",
      "                 sheep       4952        242      0.693      0.777      0.784      0.547\n",
      "                  sofa       4952        239      0.648      0.585      0.665      0.466\n",
      "                 train       4952        282      0.788      0.739      0.786      0.497\n",
      "             tvmonitor       4952        308      0.747      0.685      0.715      0.489\n",
      "Speed: 0.1ms pre-process, 1.4ms inference, 1.0ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp169\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/increment_VOC_Replay_Lwf/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# 这个新数据集上有点差。明天换5e-5跑一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f8cd273-cb4e-44e2-afbd-fac130780b38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_Replay_Lwf/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 3868f729 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.871      0.813      0.878       0.59\n",
      "                   car       2244       8711      0.926        0.9      0.958      0.741\n",
      "                   van       2244        861      0.896      0.867      0.933      0.695\n",
      "                 truck       2244        333      0.925      0.958      0.974      0.761\n",
      "                  tram       2244        138      0.886      0.928       0.97       0.66\n",
      "                person       2244       1286      0.875       0.69      0.789      0.423\n",
      "        person_sitting       2244         89       0.68      0.573      0.626      0.335\n",
      "               cyclist       2244        496      0.875      0.786      0.874      0.508\n",
      "                  misc       2244        284      0.903      0.799      0.902      0.601\n",
      "Speed: 0.1ms pre-process, 0.7ms inference, 1.1ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp168\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/increment_VOC_Replay_Lwf/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b08f4d2b-3c5b-44ce-9783-4f104070e341",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a54e58c-440d-4ebb-96b4-fd20bd8b28f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cc08509-db4f-4ef3-ac2b-9543e2041b49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "672553c6-398b-4f7f-be70-0ba8ad7dbbb0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC_base.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_Replay_Lwf_5e-5, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=5e-05, Lwf_temperature=1.0, BiC_enable=False\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/33f5e4e59e714ba79db3f83ef42e4345\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/best.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old... 18599 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.17 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_Replay_Lwf_5e-5/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_Replay_Lwf_5e-5\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.51G    0.06721    0.04596    0.05694         64        640: 1\n",
      "tensor([1.40156], device='cuda:0', grad_fn=<AddBackward0>) tensor(4332.60645, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.396      0.131      0.108      0.051\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.08G    0.04915    0.03778      0.039         21        640: 1\n",
      "tensor([1.10999], device='cuda:0', grad_fn=<AddBackward0>) tensor(6595.71094, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.369      0.368      0.291      0.143\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.08G    0.04661    0.03628    0.02998         31        640: 1\n",
      "tensor([1.25779], device='cuda:0', grad_fn=<AddBackward0>) tensor(10339.24805, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.474       0.48      0.459      0.225\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.08G    0.04453    0.03555    0.02485         29        640: 1\n",
      "tensor([1.03071], device='cuda:0', grad_fn=<AddBackward0>) tensor(9909.98047, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.545       0.52      0.526      0.277\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.08G    0.04312    0.03539    0.02246         44        640: 1\n",
      "tensor([1.09171], device='cuda:0', grad_fn=<AddBackward0>) tensor(9084.64355, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.625      0.586      0.619      0.339\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.08G    0.04186    0.03494     0.0206         30        640: 1\n",
      "tensor([1.10139], device='cuda:0', grad_fn=<AddBackward0>) tensor(10992.90137, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.656      0.597      0.642      0.363\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.08G    0.04083    0.03413    0.01908         44        640: 1\n",
      "tensor([1.07881], device='cuda:0', grad_fn=<AddBackward0>) tensor(9356.50098, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.643      0.615      0.643      0.372\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.08G    0.03999    0.03376    0.01803         41        640: 1\n",
      "tensor([1.10404], device='cuda:0', grad_fn=<AddBackward0>) tensor(10008.03516, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.679      0.628      0.679        0.4\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.689      0.643      0.688      0.415\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.08G    0.03891    0.03321    0.01657         33        640: 1\n",
      "tensor([1.01049], device='cuda:0', grad_fn=<AddBackward0>) tensor(8731.46973, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.705      0.644      0.701      0.426\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.08G    0.03832    0.03329    0.01582         59        640: 1\n",
      "tensor([0.98837], device='cuda:0', grad_fn=<AddBackward0>) tensor(8218.64258, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.701      0.651       0.71       0.43\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.08G    0.03798    0.03289    0.01547         34        640: 1\n",
      "tensor([1.01493], device='cuda:0', grad_fn=<AddBackward0>) tensor(9354.10156, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.72      0.661      0.717      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.08G    0.03762    0.03251    0.01509         56        640: 1\n",
      "tensor([1.18992], device='cuda:0', grad_fn=<AddBackward0>) tensor(9472.88574, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.721       0.67      0.728      0.447\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.08G    0.03724    0.03233    0.01456         36        640: 1\n",
      "tensor([1.04845], device='cuda:0', grad_fn=<AddBackward0>) tensor(8826.92383, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.726      0.656      0.718      0.451\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.08G    0.03677    0.03197    0.01409         69        640: 1\n",
      "tensor([1.21724], device='cuda:0', grad_fn=<AddBackward0>) tensor(8603.97656, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.724      0.686      0.735      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.08G    0.03646    0.03193    0.01375         51        640: 1\n",
      "tensor([1.03207], device='cuda:0', grad_fn=<AddBackward0>) tensor(8737.71484, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.731       0.68      0.738      0.474\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.08G    0.03619    0.03183    0.01334         43        640: 1\n",
      "tensor([1.05734], device='cuda:0', grad_fn=<AddBackward0>) tensor(8952.10449, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.727      0.695      0.744      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.08G    0.03606    0.03155    0.01313         27        640: 1\n",
      "tensor([1.05182], device='cuda:0', grad_fn=<AddBackward0>) tensor(10368.14648, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.748      0.689      0.747      0.477\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.08G    0.03572    0.03141    0.01306         33        640: 1\n",
      "tensor([1.02611], device='cuda:0', grad_fn=<AddBackward0>) tensor(9002.16113, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.744      0.694      0.754      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.08G    0.03563     0.0315    0.01275         46        640: 1\n",
      "tensor([1.01385], device='cuda:0', grad_fn=<AddBackward0>) tensor(7744.75098, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.73      0.702      0.754      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.08G    0.03521    0.03092    0.01244         46        640: 1\n",
      "tensor([1.10088], device='cuda:0', grad_fn=<AddBackward0>) tensor(9999.59766, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.733      0.711      0.758      0.492\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.08G    0.03509    0.03064    0.01231         29        640: 1\n",
      "tensor([1.10207], device='cuda:0', grad_fn=<AddBackward0>) tensor(9699.26172, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.734      0.699      0.752       0.49\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.08G    0.03483    0.03061    0.01208         35        640: 1\n",
      "tensor([0.97034], device='cuda:0', grad_fn=<AddBackward0>) tensor(9336.96484, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.754      0.699       0.76      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.08G    0.03462    0.03076    0.01186         37        640: 1\n",
      "tensor([0.90230], device='cuda:0', grad_fn=<AddBackward0>) tensor(8056.05469, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.752      0.703      0.765      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.08G    0.03416    0.03062    0.01152         31        640: 1\n",
      "tensor([0.96276], device='cuda:0', grad_fn=<AddBackward0>) tensor(9590.59863, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.701      0.767      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.08G    0.03405    0.03004    0.01128         47        640: 1\n",
      "tensor([1.20449], device='cuda:0', grad_fn=<AddBackward0>) tensor(9900.56445, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.746      0.713      0.768      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.08G    0.03394    0.03026    0.01118         40        640: 1\n",
      "tensor([0.85734], device='cuda:0', grad_fn=<AddBackward0>) tensor(8734.72656, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.709      0.771      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.08G    0.03355    0.02984    0.01104         31        640: 1\n",
      "tensor([1.01379], device='cuda:0', grad_fn=<AddBackward0>) tensor(9594.68555, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.746      0.726       0.77      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.08G    0.03341    0.02989    0.01082         26        640: 1\n",
      "tensor([0.82304], device='cuda:0', grad_fn=<AddBackward0>) tensor(8699.21875, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757       0.72      0.776      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.721      0.776      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.08G    0.03298     0.0298    0.01057         40        640: 1\n",
      "tensor([1.00509], device='cuda:0', grad_fn=<AddBackward0>) tensor(8883.30762, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.74      0.737      0.776      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.08G    0.03265    0.02949    0.01017         40        640: 1\n",
      "tensor([0.93917], device='cuda:0', grad_fn=<AddBackward0>) tensor(9826.14746, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.751       0.72      0.776       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.08G    0.03249     0.0296    0.01004         33        640: 1\n",
      "tensor([0.75911], device='cuda:0', grad_fn=<AddBackward0>) tensor(7294.31592, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.753      0.733      0.779      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.08G    0.03224    0.02904   0.009963         36        640: 1\n",
      "tensor([1.02257], device='cuda:0', grad_fn=<AddBackward0>) tensor(8682.40625, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.723       0.78      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.08G    0.03219    0.02906   0.009612         44        640: 1\n",
      "tensor([1.04019], device='cuda:0', grad_fn=<AddBackward0>) tensor(9653.92480, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.758      0.727      0.781      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.732      0.783      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.08G    0.03162    0.02864   0.009252         56        640: 1\n",
      "tensor([0.94597], device='cuda:0', grad_fn=<AddBackward0>) tensor(8057.06592, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.728      0.786      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.724      0.788      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.08G     0.0311      0.028   0.009062         34        640: 1\n",
      "tensor([0.81306], device='cuda:0', grad_fn=<AddBackward0>) tensor(7758.79443, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771       0.73      0.789      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.08G    0.03096      0.028    0.00893         48        640: 1\n",
      "tensor([0.99508], device='cuda:0', grad_fn=<AddBackward0>) tensor(8401.68652, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.739      0.789      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.08G    0.03063    0.02807   0.008691         56        640: 1\n",
      "tensor([1.05590], device='cuda:0', grad_fn=<AddBackward0>) tensor(9693.88477, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.737      0.788      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.08G    0.03043    0.02784   0.008554         36        640: 1\n",
      "tensor([0.83376], device='cuda:0', grad_fn=<AddBackward0>) tensor(7974.43018, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.731      0.787      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.08G    0.03043    0.02774   0.008531         41        640: 1\n",
      "tensor([0.95759], device='cuda:0', grad_fn=<AddBackward0>) tensor(8105.65137, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.733      0.787      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.08G    0.02997    0.02766   0.008269         38        640: 1\n",
      "tensor([0.80639], device='cuda:0', grad_fn=<AddBackward0>) tensor(8076.54248, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771      0.733      0.787      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.08G    0.02975    0.02715   0.008085         21        640: 1\n",
      "tensor([0.70111], device='cuda:0', grad_fn=<AddBackward0>) tensor(8332.90625, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.733      0.787      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.08G    0.02956    0.02704   0.007999         40        640: 1\n",
      "tensor([0.87403], device='cuda:0', grad_fn=<AddBackward0>) tensor(9127.64062, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77       0.73      0.787      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.08G    0.02921     0.0268   0.007881         40        640: 1\n",
      "tensor([0.96309], device='cuda:0', grad_fn=<AddBackward0>) tensor(8084.75146, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774      0.729      0.787      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.08G    0.02924    0.02687   0.007666         31        640: 1\n",
      "tensor([0.74612], device='cuda:0', grad_fn=<AddBackward0>) tensor(7675.78516, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.778      0.723      0.787      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.08G    0.02892    0.02662   0.007586         26        640: 1\n",
      "tensor([0.71183], device='cuda:0', grad_fn=<AddBackward0>) tensor(7485.75977, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.729      0.787      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.08G    0.02866    0.02659   0.007584         30        640: 1\n",
      "tensor([0.75120], device='cuda:0', grad_fn=<AddBackward0>) tensor(6816.54297, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.727      0.787      0.536\n",
      "\n",
      "50 epochs completed in 2.125 hours.\n",
      "Optimizer stripped from runs/train/increment_VOC_Replay_Lwf_5e-5/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/increment_VOC_Replay_Lwf_5e-5/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/increment_VOC_Replay_Lwf_5e-5/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.777      0.723      0.787      0.537\n",
      "                   car       4952       1201       0.84      0.874       0.91      0.691\n",
      "                person       4952       4528      0.847       0.79      0.874      0.573\n",
      "             aeroplane       4952        285      0.906      0.775      0.877      0.568\n",
      "               bicycle       4952        337      0.894      0.789      0.877      0.594\n",
      "                  bird       4952        459       0.76      0.675       0.74      0.462\n",
      "                  boat       4952        263      0.642      0.597      0.647      0.383\n",
      "                bottle       4952        469      0.688      0.688      0.725      0.482\n",
      "                   bus       4952        213      0.805      0.814      0.869      0.712\n",
      "                   cat       4952        358      0.868      0.754      0.829      0.575\n",
      "                 chair       4952        756      0.669      0.574      0.621      0.399\n",
      "                   cow       4952        244      0.756      0.803      0.834      0.605\n",
      "           diningtable       4952        206      0.747      0.665      0.734      0.491\n",
      "                   dog       4952        489      0.814      0.663      0.794      0.526\n",
      "                 horse       4952        348      0.874       0.83      0.883       0.61\n",
      "             motorbike       4952        325      0.818      0.772      0.858       0.55\n",
      "           pottedplant       4952        480      0.648      0.486      0.533      0.284\n",
      "                 sheep       4952        242      0.692      0.785      0.812      0.582\n",
      "                  sofa       4952        239      0.677      0.622      0.696      0.506\n",
      "                 train       4952        282      0.844      0.766      0.846      0.583\n",
      "             tvmonitor       4952        308      0.759       0.74       0.78      0.554\n",
      "Results saved to \u001b[1mruns/train/increment_VOC_Replay_Lwf_5e-5\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : increment_VOC_Replay_Lwf_5e-5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/33f5e4e59e714ba79db3f83ef42e4345\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8355318008394579\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 23.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8769140729720971\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.5682206964779638\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9057364802827934\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.7754275192871684\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 221.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8382187136521745\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 32.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.8769078616481415\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.5936732642621927\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8935793345889838\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.7893175074183977\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 266.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.7153029329790674\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 98.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.739876151680227\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.4624976305819185\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7602406244430622\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.6753812636165577\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 310.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6187254618799731\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 87.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6465038457106419\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.38298185596685114\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6421402533346328\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.596958174904943\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 157.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6880582120570624\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 146.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.724880716975593\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.4824410674344784\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6883945275321727\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.6877222250356578\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 323.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8093733660628532\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8691258410473175\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7122257685503126\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8049641203949709\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.813831181661351\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 173.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.857013541368551\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 199.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9101326262630286\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.6912841583320214\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8404237864766287\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8742714404662781\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1050.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.8072348879424639\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 41.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8286112293343291\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.57529311969249\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8683060347825413\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.7541899441340782\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 270.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6178026311699227\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 215.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6205959024837366\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.3992254436312827\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.668742286104705\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.5740740740740741\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 434.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.7787996608000336\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 63.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8336720112280444\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6046447052795388\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7557684547908815\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8032786885245902\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 196.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.7034289680623874\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 47.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7344645431690477\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.49082122002417006\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7465106272027948\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6650485436893204\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 137.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7305020424322276\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 74.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.7944304434284544\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.526072492197561\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.8140538708223393\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.6625047231845059\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 324.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.851620589339722\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.8826452659653231\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6103103917710448\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8738879966053514\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8304597701149425\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 289.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5815]                 : (0.8573375344276428, 5.554547309875488)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.10849527958714562, 0.7890094899325485)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.051027469048087325, 0.5365718077071311)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.3688470676193643, 0.7777036553373363)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.13117400970770576, 0.7391912574043926)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.7944131703873393\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 56.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8580319120847769\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5501301217588994\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8178213696583188\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.7723076923076924\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 251.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8171859211606506\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 648.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8736025878069077\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5731728108660856\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8465936516098328\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.7897526501766784\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3576.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5553114485188638\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 127.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5330155363291011\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.28373802642559\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6475345947157787\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.48608261038816586\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 233.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7355230669821293\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 85.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8120339582306089\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.5824097273068762\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.6918169219872383\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.7851239669421488\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 190.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6484121843270518\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 71.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.6955529357592998\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.5057255645732064\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6768600226996979\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6222591722359272\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 149.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.028660159558057785, 0.06720925122499466)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.0075839548371732235, 0.05693947896361351)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.026585517451167107, 0.04595797881484032)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.8029778210906586\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 40.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8462345089982645\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.5827408035491397\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8437584873476418\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.7659574468085106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 216.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7495207888080793\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 72.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7797783020333577\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5541840633739958\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7590164944664439\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7402597402597403\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 228.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.031032733619213104, 0.048601940274238586)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.00836923811584711, 0.047636449337005615)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.01819760352373123, 0.02474883385002613)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002579535683577)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : increment_VOC_Replay_Lwf_5e-5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/33f5e4e59e714ba79db3f83ef42e4345\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     BiC_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/increment_VOC_Replay_Lwf_5e-5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.04 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 5e-5 \\\n",
    "--name increment_VOC_Replay_Lwf_5e-5 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f23cc830-e52a-47bc-adbb-38265f6ba060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_Replay_Lwf_5e-5/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.852       0.79       0.86      0.568\n",
      "                   car       2244       8711      0.916      0.892      0.953      0.725\n",
      "                   van       2244        861      0.894      0.864       0.92      0.675\n",
      "                 truck       2244        333      0.912      0.958      0.971      0.742\n",
      "                  tram       2244        138      0.875      0.884      0.949      0.623\n",
      "                person       2244       1286      0.861      0.665      0.771      0.411\n",
      "        person_sitting       2244         89      0.578      0.508      0.573      0.322\n",
      "               cyclist       2244        496      0.876      0.768      0.869      0.488\n",
      "                  misc       2244        284      0.904      0.778      0.876      0.554\n",
      "Speed: 0.1ms pre-process, 1.2ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp182\u001b[0m\n",
      "Test set val successful|ly!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/increment_VOC_Replay_Lwf_5e-5/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "781bb57a-6dfe-4a3a-bdc9-6f7023d9ba98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51d0b3c3-b60a-480c-8598-05d0a6d3a2a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10017a5c-3d60-49c1-a359-13741e42df6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6157bed-2ef1-4163-8257-7580ba63b8ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fc97d50-9e9c-4307-8670-616c1cb4db6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac4038e6-96b8-4d31-a7e6-97d1d5140c63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1361458-24d3-4cc1-b6b4-a1eb4b3a9d11",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_BiC_base, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=False\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/3a4d0d03a9ab4991b3304b4a476b2188\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old... 15041 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.08 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_BiC_base/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_BiC_base\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.67G    0.06728    0.04463    0.06279          4        640: 1\n",
      "tensor([0.09666], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.636      0.131      0.109     0.0525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.24G    0.04888      0.036    0.04175          2        640: 1\n",
      "tensor([0.07055], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.346      0.426      0.352      0.178\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.24G    0.04754    0.03538     0.0307          5        640: 1\n",
      "tensor([0.12867], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.491      0.472      0.458      0.226\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.24G    0.04667    0.03611    0.02761          3        640: 1\n",
      "tensor([0.09740], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.557      0.512      0.519      0.265\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.24G    0.04499     0.0356     0.0247          1        640: 1\n",
      "tensor([0.02450], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.578      0.531      0.541       0.29\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.24G    0.04359    0.03523     0.0225          3        640: 1\n",
      "tensor([0.08290], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.634       0.59      0.614      0.349\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.24G    0.04249    0.03514    0.02105          6        640: 1\n",
      "tensor([0.13936], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.637      0.595      0.628      0.358\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.24G    0.04163    0.03471    0.01983          3        640: 1\n",
      "tensor([0.09726], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.633      0.625      0.651      0.376\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.24G    0.04086    0.03402      0.019          4        640: 1\n",
      "tensor([0.11228], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.661      0.641      0.668      0.394\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.24G    0.04033    0.03371    0.01833          2        640: 1\n",
      "tensor([0.11616], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.672      0.643      0.678      0.403\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.24G    0.03973    0.03324    0.01745          7        640: 1\n",
      "tensor([0.11882], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.7      0.656      0.702      0.427\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.24G    0.03915     0.0331    0.01651          7        640: 1\n",
      "tensor([0.07584], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.696      0.655      0.699      0.431\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.24G    0.03863      0.033    0.01614          2        640: 1\n",
      "tensor([0.03864], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.694      0.663      0.706      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.24G    0.03837    0.03262    0.01568          4        640: 1\n",
      "tensor([0.07219], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.713      0.678      0.722      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.24G    0.03804    0.03218    0.01496          3        640: 1\n",
      "tensor([0.08219], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.682      0.729      0.461\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.24G    0.03748    0.03209    0.01471          3        640: 1\n",
      "tensor([0.06771], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.723      0.685      0.733      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.24G    0.03713    0.03189    0.01408          1        640: 1\n",
      "tensor([0.07789], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.724       0.69      0.742      0.472\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.24G    0.03706    0.03162    0.01375          7        640: 1\n",
      "tensor([0.13593], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.733      0.699      0.748      0.477\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.24G    0.03656    0.03158    0.01335          2        640: 1\n",
      "tensor([0.03471], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.741      0.698      0.753      0.485\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.24G    0.03606    0.03134      0.013          6        640: 1\n",
      "tensor([0.07806], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.734      0.694      0.744      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.24G    0.03605    0.03123    0.01286          5        640: 1\n",
      "tensor([0.08522], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.745      0.705      0.758      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.24G    0.03573    0.03095    0.01251          4        640: 1\n",
      "tensor([0.05140], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.706      0.756      0.495\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.24G    0.03544    0.03086    0.01197          5        640: 1\n",
      "tensor([0.06046], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.734      0.716      0.763      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.24G    0.03514    0.03068    0.01186          2        640: 1\n",
      "tensor([0.05031], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.711      0.769      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.24G    0.03499    0.03048    0.01143         12        640: 1\n",
      "tensor([0.13371], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.748      0.716      0.772      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.24G    0.03448    0.02995     0.0115          1        640: 1\n",
      "tensor([0.06557], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.756      0.727       0.78      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.24G    0.03436     0.0298    0.01101          2        640: 1\n",
      "tensor([0.03711], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.745      0.731      0.777      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.24G    0.03409    0.02989    0.01091         24        640: 1\n",
      "tensor([0.18197], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.734       0.78      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.24G    0.03382    0.02983    0.01061          3        640: 1\n",
      "tensor([0.08009], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.759      0.729      0.781      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.24G    0.03359    0.02955    0.01039          3        640: 1\n",
      "tensor([0.11080], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.733      0.789      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.24G    0.03326     0.0296   0.009851          4        640: 1\n",
      "tensor([0.07631], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.761      0.737       0.79      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.24G    0.03294    0.02908   0.009634          7        640: 1\n",
      "tensor([0.08090], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.743      0.793      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.24G    0.03287    0.02906   0.009605          4        640: 1\n",
      "tensor([0.06478], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.777      0.739      0.799      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.24G    0.03258    0.02868   0.009242          2        640: 1\n",
      "tensor([0.06211], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771      0.752      0.801      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.24G    0.03232    0.02837   0.008961          8        640: 1\n",
      "tensor([0.07491], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.752        0.8      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.24G    0.03189    0.02835    0.00904          3        640: 1\n",
      "tensor([0.04203], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.751      0.801      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.24G     0.0316    0.02829   0.008798          2        640: 1\n",
      "tensor([0.06690], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.753      0.797      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.24G    0.03151    0.02806   0.008691          1        640: 1\n",
      "tensor([0.00607], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.755        0.8      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.24G    0.03125    0.02776   0.008272          5        640: 1\n",
      "tensor([0.06036], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.757      0.802      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.24G    0.03095    0.02763   0.008027          1        640: 1\n",
      "tensor([0.01160], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.775      0.755      0.803      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.24G    0.03073    0.02764     0.0079          3        640: 1\n",
      "tensor([0.08327], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.774      0.761      0.804      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.24G    0.03033    0.02733   0.007569         15        640: 1\n",
      "tensor([0.13996], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77       0.76      0.806      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.24G    0.03023    0.02686    0.00746          5        640: 1\n",
      "tensor([0.12566], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.761      0.804      0.557\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.24G    0.02973     0.0265    0.00753          5        640: 1\n",
      "tensor([0.10167], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.753      0.803      0.557\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.24G    0.02959    0.02645   0.007214          2        640: 1\n",
      "tensor([0.03315], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766       0.76      0.803      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.24G    0.02927    0.02645   0.007103          2        640: 1\n",
      "tensor([0.10153], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.755      0.802      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.24G      0.029    0.02603   0.006888         10        640: 1\n",
      "tensor([0.09754], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.775      0.755      0.802      0.557\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.24G    0.02864    0.02599   0.006714          2        640: 1\n",
      "tensor([0.02054], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.777      0.755      0.803      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.24G    0.02861    0.02575   0.006692          3        640: 1\n",
      "tensor([0.05767], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.758      0.804      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.24G    0.02824    0.02551   0.006481          1        640: 1\n",
      "tensor([0.08140], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.776      0.757      0.805      0.558\n",
      "\n",
      "50 epochs completed in 1.512 hours.\n",
      "Optimizer stripped from runs/train/increment_VOC_BiC_base/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/increment_VOC_BiC_base/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/increment_VOC_BiC_base/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.775      0.757      0.805      0.558\n",
      "                   car       4952       1201      0.815      0.877      0.916      0.704\n",
      "                person       4952       4528      0.857      0.805      0.882      0.586\n",
      "             aeroplane       4952        285      0.942        0.8      0.886        0.6\n",
      "               bicycle       4952        337      0.882      0.821      0.905       0.63\n",
      "                  bird       4952        459      0.785      0.749      0.788      0.511\n",
      "                  boat       4952        263      0.617      0.673      0.658      0.385\n",
      "                bottle       4952        469      0.696       0.74      0.749      0.496\n",
      "                   bus       4952        213      0.835      0.803      0.883      0.716\n",
      "                   cat       4952        358      0.866      0.804      0.855      0.629\n",
      "                 chair       4952        756      0.595      0.618      0.627      0.408\n",
      "                   cow       4952        244      0.751      0.828      0.851      0.613\n",
      "           diningtable       4952        206      0.744      0.694      0.763      0.527\n",
      "                   dog       4952        489       0.81      0.748      0.814      0.577\n",
      "                 horse       4952        348      0.855      0.831      0.885      0.624\n",
      "             motorbike       4952        325      0.848      0.791      0.868      0.569\n",
      "           pottedplant       4952        480      0.612       0.51      0.536      0.287\n",
      "                 sheep       4952        242      0.693      0.831      0.828      0.596\n",
      "                  sofa       4952        239      0.682      0.661      0.734       0.54\n",
      "                 train       4952        282      0.851      0.798      0.867      0.594\n",
      "             tvmonitor       4952        308      0.771      0.763      0.798      0.571\n",
      "Results saved to \u001b[1mruns/train/increment_VOC_BiC_base\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : increment_VOC_BiC_base\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/3a4d0d03a9ab4991b3304b4a476b2188\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8652648025841031\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 14.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8858708516949072\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.5999838757016815\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9421242435394799\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 228.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8505242291376133\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 37.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.904609419973461\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6304191830364515\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8820645165423456\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.8211616661171557\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 277.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.7668943621846561\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 94.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.7881828576338558\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.5108428299848239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7851642959625873\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.7494553376906318\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 344.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6437330038117499\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 110.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6582855536893661\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.3850528489280373\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6169022185282348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6730038022813688\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 177.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.7174887340835583\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 151.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7494064090825662\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.49564758712146606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6964199557824559\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.7398720682302772\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 347.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8185161269353248\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 34.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.883401337130462\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7161656133461718\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8348416010898077\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8028169014084507\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 171.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8446697117655907\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 239.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9160470027644979\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.7042230581315769\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.814837470279356\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8767693588676103\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1053.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.834055561138162\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 45.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8549443067598792\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.6294050718303827\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8659011532728623\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.8044692737430168\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 288.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6063276443930562\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 318.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.627312563632676\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.40752001296586365\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.5950342602046789\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.6180580050950422\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 467.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.7876409700955503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 67.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.850945163262561\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6131779657336267\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7511414337896923\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8278688524590164\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 202.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.7181423437845281\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 49.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7630307389901506\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.5271992489279957\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7438241620290411\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6941747572815534\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 143.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7777343050416635\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 86.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.8137285295096036\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.5768344287400122\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.8096820579553151\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.7482119864532952\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 366.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8430286282250031\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 49.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.8851662930093337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6243806521883015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8551470229869893\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8312488967801556\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 289.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [4705]                 : (0.07631278038024902, 3.869253158569336)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.10859195655562323, 0.8056994653471218)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.052524781457103334, 0.5583748701951022)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.34561276451463296, 0.7773843759030726)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.1310025442580301, 0.7608273318632236)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.8182018178840104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 46.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8681628079076676\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5685595509519343\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8476061348824941\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.7907692307692308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 257.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8305543428729202\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 607.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8822310353353764\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5857054937557061\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8572933542142402\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8054328621908127\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3647.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5567363490543854\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 155.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5361867698380682\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.28682882655751385\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6123019996305532\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.5104166666666666\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 245.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7556693900374838\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 89.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8278953226136516\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.595964644017638\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.6931171193708224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8306320783593512\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 201.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6714159252150798\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 74.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.7335187269907206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.5396471266870224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6820718128189392\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6610878661087866\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 158.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.02824300155043602, 0.06728269159793854)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.006480729207396507, 0.06278747320175171)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.02551109716296196, 0.044626086950302124)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.8237021202371887\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 39.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8670467109174068\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.5939637850386675\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8512602439489232\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.7978723404255319\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 225.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7668203231352827\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 70.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7980458594652164\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5714543697949759\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7706923455143904\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.762987012987013\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 235.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.030580656602978706, 0.045748598873615265)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.006046602968126535, 0.04613499715924263)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.018003476783633232, 0.023092184215784073)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07003188097768331)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.009600597945448104)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.009600597945448104)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : increment_VOC_BiC_base\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/3a4d0d03a9ab4991b3304b4a476b2188\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     BiC_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : 0.0001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/increment_VOC_BiC_base\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.00 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name increment_VOC_BiC_base \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "854c79ac-1a41-449f-a8b5-21273acc827f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/increment_VOC_BiC_base/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.763      0.703      0.752      0.461\n",
      "                   car       2244       8711      0.864      0.865      0.922      0.663\n",
      "                   van       2244        861      0.785      0.729      0.788      0.543\n",
      "                 truck       2244        333      0.885       0.85      0.911      0.661\n",
      "                  tram       2244        138      0.722      0.904      0.891      0.514\n",
      "                person       2244       1286      0.844      0.597      0.703      0.359\n",
      "        person_sitting       2244         89      0.446      0.449      0.445      0.201\n",
      "               cyclist       2244        496      0.786      0.652      0.708      0.373\n",
      "                  misc       2244        284      0.775      0.574      0.646      0.372\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.9ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp170\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/increment_VOC_BiC_base/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d3cd4a8-227e-4808-98c3-ff3231742a78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a71fb90-a90d-48aa-ae68-1e430dfdd449",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "590e9ace-c32e-4a46-ab8c-2265c697b007",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/increment_VOC_BiC_base/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_BiC, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=True\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/982af737237742b885147cc78b54257b\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 354/355 items from runs/train/increment_VOC_BiC_base/weights/last.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning ../datasets/VOC/labels/val.cache... 3558 images, 0 backgrounds, \u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.45 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_BiC/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_BiC\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49     0.952G    0.03442     0.0329    0.01135         45        640: 1\n",
      "tensor([0.44443], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \u001b[1;38;5;214mCOMET WARNING:\u001b[0m Unknown error retrieving Conda package as an explicit file\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.759      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      3.53G    0.03386    0.03301    0.01181         27        640: 1\n",
      "tensor([0.42573], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.765      0.758      0.797       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      3.53G    0.03466    0.03354    0.01125         46        640: 1\n",
      "tensor([0.46322], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.761      0.797       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      3.53G    0.03457    0.03332    0.01166         21        640: 1\n",
      "tensor([0.27208], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.758      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      3.53G    0.03511    0.03361    0.01139         31        640: 1\n",
      "tensor([0.37342], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771      0.751      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      3.53G    0.03462    0.03359    0.01154         38        640: 1\n",
      "tensor([0.44351], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.756      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      3.53G     0.0348    0.03349     0.0113         34        640: 1\n",
      "tensor([0.35914], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.756      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      3.53G    0.03449    0.03296      0.011         26        640: 1\n",
      "tensor([0.37722], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.754      0.799      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      3.53G    0.03479    0.03389    0.01152         52        640: 1\n",
      "tensor([0.50225], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.759      0.797       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      3.53G    0.03457    0.03353    0.01138         35        640: 1\n",
      "tensor([0.37393], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.751      0.799      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      3.53G    0.03457    0.03334    0.01152         38        640: 1\n",
      "tensor([0.40973], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.756      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      3.53G    0.03484    0.03396    0.01159         35        640: 1\n",
      "tensor([0.51584], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.753      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      3.53G    0.03417    0.03308    0.01119         32        640: 1\n",
      "tensor([0.44392], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.765      0.761      0.798      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      3.53G    0.03484    0.03418    0.01189         56        640: 1\n",
      "tensor([0.66931], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.759      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      3.53G    0.03471    0.03294    0.01141         31        640: 1\n",
      "tensor([0.33999], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.752      0.796       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      3.53G    0.03454     0.0336    0.01148         30        640: 1\n",
      "tensor([0.44743], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.755      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      3.53G    0.03448    0.03323    0.01168         35        640: 1\n",
      "tensor([0.32783], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.756      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      3.53G    0.03451    0.03318    0.01085         40        640: 1\n",
      "tensor([0.45902], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.772      0.754      0.799      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      3.53G    0.03463    0.03334    0.01151         61        640: 1\n",
      "tensor([0.61602], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.765       0.76      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      3.53G    0.03447    0.03283    0.01173         53        640: 1\n",
      "tensor([0.52885], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.755      0.798      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      3.53G    0.03463    0.03334    0.01133         57        640: 1\n",
      "tensor([0.52757], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.755      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      3.53G    0.03471    0.03351    0.01145         28        640: 1\n",
      "tensor([0.37336], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.754      0.799      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      3.53G    0.03413    0.03287    0.01117         66        640: 1\n",
      "tensor([0.76486], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.761      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      3.53G    0.03464    0.03324     0.0111         45        640: 1\n",
      "tensor([0.43424], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766       0.76      0.798      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      3.53G    0.03455    0.03294    0.01118         33        640: 1\n",
      "tensor([0.46000], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.758      0.798      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      3.53G    0.03468    0.03346    0.01109         62        640: 1\n",
      "tensor([0.61741], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.758      0.799      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      3.53G    0.03464    0.03375    0.01151         56        640: 1\n",
      "tensor([0.54811], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.756      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      3.53G    0.03442    0.03308    0.01119         58        640: 1\n",
      "tensor([0.56086], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.759      0.799      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      3.53G    0.03481    0.03345    0.01119         49        640: 1\n",
      "tensor([0.47670], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.77      0.754      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      3.53G    0.03451    0.03357    0.01112         53        640: 1\n",
      "tensor([0.51850], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.763      0.798      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      3.53G    0.03477    0.03355    0.01117         33        640: 1\n",
      "tensor([0.52377], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.764      0.799      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      3.53G    0.03492     0.0338    0.01099         50        640: 1\n",
      "tensor([0.56605], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.759      0.799      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      3.53G    0.03473    0.03355    0.01127         39        640: 1\n",
      "tensor([0.47859], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.773      0.751      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      3.53G    0.03477    0.03393    0.01127         46        640: 1\n",
      "tensor([0.52663], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.759      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      3.53G    0.03461    0.03409    0.01116         37        640: 1\n",
      "tensor([0.37726], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.769      0.755      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      3.53G    0.03456    0.03307    0.01167         56        640: 1\n",
      "tensor([0.62995], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.761      0.798      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      3.53G    0.03449     0.0329    0.01156         38        640: 1\n",
      "tensor([0.44909], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.765      0.756      0.797       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      3.53G    0.03453    0.03322     0.0117         19        640: 1\n",
      "tensor([0.45844], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.771      0.751      0.797       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      3.53G    0.03441    0.03297    0.01144         34        640: 1\n",
      "tensor([0.52329], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   ^C\n",
      "                 Class     Images  Instances          P          R      mAP50   \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/increment_VOC_BiC_base/weights/last.pt \\\n",
    "--name increment_VOC_BiC \\\n",
    "--BiC_enable \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93e078b7-4c9d-464d-88a1-7b9908c0d954",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc395092-7061-4519-9e87-267f462e52ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c538ee2-6ec8-4b4a-ab5d-d02a59126193",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_BiC: \u001b[0mweights=./runs/train/increment_VOC_BiC_base/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=increment_VOC_BiC2, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=0.0001, Lwf_temperature=1.0, BiC_enable=True\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 a471430d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/f9e985fbdbb74f2e8d74a561642bd769\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo.Detect                      [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 354/355 items from runs/train/increment_VOC_BiC_base/weights/last.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning ../datasets/VOC/labels/val.cache... 3558 images, 0 backgrounds, \u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.45 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/increment_VOC_BiC2/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/increment_VOC_BiC2\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      3.78G    0.03458    0.03221    0.01051         45        640: 1\n",
      "tensor([0.43075], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.759      0.752      0.793      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.35G    0.03644    0.03185    0.01015         27        640: 1\n",
      "tensor([0.44756], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.733       0.78      0.486\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.35G    0.03999    0.03244    0.01025         46        640: 1\n",
      "tensor([0.52245], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.696      0.764      0.481\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.35G    0.04116    0.03273    0.01099         21        640: 1\n",
      "tensor([0.38880], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.69      0.676       0.71      0.408\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.35G    0.04108    0.03282    0.01061         31        640: 1\n",
      "tensor([0.43454], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.724      0.692      0.735      0.396\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.35G    0.04003    0.03255    0.01068         38        640: 1\n",
      "tensor([0.48590], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.721      0.651      0.708      0.373\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.35G    0.03941    0.03222   0.009775         34        640: 1\n",
      "tensor([0.39233], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.703      0.659      0.699      0.388\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.35G    0.03889     0.0315   0.009379         26        640: 1\n",
      "tensor([0.43282], device='cuda:0', grad_fn=<MulBackward0>)  \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.661      0.654      0.678       0.35\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.35G    0.03899    0.03261   0.009305        130        640:  ^C\n",
      "       8/49      6.35G      0.039    0.03265   0.009283        114        640:  \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_BiC.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/increment_VOC_BiC_base/weights/last.pt \\\n",
    "--name increment_VOC_BiC2 \\\n",
    "--BiC_enable \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1cb0555-8737-4e11-acee-de1367f03497",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "543c69c1-1dae-4e83-9bef-25d205e49efe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ca66490-ac48-44f8-b4c9-9ff0328c930f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7622a41f-25de-411c-ad58-edc4a8a58573",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86e69ff9-0c39-4007-8f83-b48165213902",
   "metadata": {},
   "outputs": [],
   "source": []
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